From owner-chemistry@ccl.net Thu May 24 02:10:00 2018 From: "Norrby, Per-Ola Per-Ola.Norrby-x-astrazeneca.com" To: CCL Subject: CCL: RateDetermining State Message-Id: <-53314-180524020911-12340-zWXnxciit+WExMXENSovUw[a]server.ccl.net> X-Original-From: "Norrby, Per-Ola" Content-Language: en-US Content-Type: multipart/alternative; boundary="_000_VI1PR04MB40642426165910D4FB8F5A7CCA6A0VI1PR04MB4064eurp_" Date: Thu, 24 May 2018 06:09:00 +0000 MIME-Version: 1.0 Sent to CCL by: "Norrby, Per-Ola" [Per-Ola.Norrby|astrazeneca.com] --_000_VI1PR04MB40642426165910D4FB8F5A7CCA6A0VI1PR04MB4064eurp_ Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: base64 U28gdGhlIGNvbmNlcHQgb2YgcmF0ZSBkZXRlcm1pbmluZyBzdGF0ZSBoYXMgYmVlbiBhcm91bmQg Zm9yIGFib3V0IGEgZGVjYWRlIG5vdywgaXQgY2FtZSBvdXQgb2YgdGhlIHNhbWUgd29yayBhcyB0 aGUgZW5lcmdldGljIHNwYW4gbW9kZWwsIGZyb20gU2ViYXN0aWFuIEtvenVjaCBhbmQgU2Fpc29u IFNoYWlrLiBJdOKAmXMgdmFsaWQsIEnigJltIG5vdCBjb250ZXN0aW5nIHRoYXQsIGJ1dCB0aGVy 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IGZhY2U9IkFyaWFsIj48L2ZvbnQ+PC9wPg0KPHAgY2xhc3M9IntJbXByaW50LlVuaXF1ZUlEfSIg c3R5bGU9IkZPTlQtU0laRTogOHB0Ij48L3A+DQo8cCBjbGFzcz0ie0ltcHJpbnQuVW5pcXVlSUR9 IiBzdHlsZT0iRk9OVC1TSVpFOiA4cHQiPjwvcD4NCjwvYm9keT4NCjwvaHRtbD4NCg== --_000_VI1PR04MB40642426165910D4FB8F5A7CCA6A0VI1PR04MB4064eurp_-- From owner-chemistry@ccl.net Thu May 24 04:01:00 2018 From: "John Keller jwkeller]_[alaska.edu" To: CCL Subject: CCL: RateDetermining State Message-Id: <-53315-180524024352-32539-Y4sdxCil1+cgq6e7NzKvqw^server.ccl.net> X-Original-From: John Keller Content-Type: multipart/alternative; boundary="_3AC69BCB-AFDB-4C0F-B2B6-D5397CA0C402_" Date: Wed, 23 May 2018 22:43:42 -0800 MIME-Version: 1.0 Sent to CCL by: John Keller [jwkeller[]alaska.edu] --_3AC69BCB-AFDB-4C0F-B2B6-D5397CA0C402_ Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="utf-8" Dear Andrew and Anyao, =E2=80=9CRate limiting step=E2=80=9D or =E2=80=9Crate determining step=E2= =80=9D or =E2=80=9Crate controlling step=E2=80=9D are still widely used in = authoritative sources. Cramer=E2=80=99s Essentials=E2=80=A6 uses the first = term on p.483. March=E2=80=99s Advanced Organic Chem 7th Ed discusses rate= -determining steps (p.279). Searching the titles of papers in JOC and J Phy= s. Chem 2012-1018, I found 11 that use =E2=80=9Crate limiting=E2=80=9D or = =E2=80=9Crate limiting step=E2=80=9D, but no mention of =E2=80=9Crate deter= mining state=E2=80=9D. Of course the latter phrase could be buried in journ= al texts. John Keller Sent from Mail for Windows 10 > From: Andrew Rosen rosen__u.northwestern.edu Sent: Wednesday, May 23, 2018 9:06 PM To: Keller, John W Subject: CCL: RateDetermining State Anyao, I refer you to the following two papers: 1) "Degree of Rate Control: How Much the Energies of Intermediates and Tran= sition States Control Rates" (https://pubs.acs.org/doi/abs/10.1021/ja900009= 7) 2) "The Rate=E2=80=90Determining Step is Dead. Long Live the Rate=E2=80=90D= etermining State!" (https://onlinelibrary.wiley.com/doi/full/10.1002/cphc.2= 01100137) > From a detailed mechanistic point of view, it is often much more insightful= to talk about about a rate-determining state (either transition state or i= ntermediate) than a rate-determining step. The first paper describes the wi= dely used definition of a rate-controlling state and how to compute it. The= second paper contains a few short examples of how the rate-determining sta= te differs from the rate-determining step. On Wed, May 23, 2018 at 10:19 PM Anyao Jiao 1425623121(_)qq.com wrote: Sent to CCL by: "Anyao=C2=A0 Jiao" [1425623121^qq.com] Dear All, =C2=A0 I have a simple question regarding RateDetermining State. My reviewe= rs=20 suggest me to use RateDetermining State rather than Rate-Determining Step=20 owning to its inaccuracy. Is that right? If we use the Canonical variationa= l=20 transition-state theory method, how do you calculate the reaction rate of t= he=20 RateDetermining State ?=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0Sincerely =C2=A0 =C2=A0Anyao Jiao -=3D This is automatically added to each message by the mailing script =3D-=

Dear Andrew and Anyao,

=E2=80=9CRate limiting step=E2=80=9D or =E2=80=9Crate determin= ing step=E2=80=9D or =E2=80=9Crate controlling step=E2=80=9D are still wide= ly used in authoritative sources. Cramer=E2=80=99s Essentials=E2=80=A6 uses= the first =C2=A0term on p.483. March=E2=80=99s Advanced Organic Chem 7th Ed discusses rate-determining steps (p.279). Searching the titles= of papers in JOC and J Phys. Chem 2012-1018, I found 11 that use =E2=80=9C= rate limiting=E2=80=9D or =E2=80=9Crate limiting step=E2=80=9D, but no ment= ion of =E2=80=9Crate determining state=E2=80=9D. Of course the latter phras= e could be buried in journal texts.

John Keller

=

 

 <= /o:p>

Sent from Mail for Windows 10

<= o:p> 

From: Andrew Rosen rosen__u.northwestern.edu
Sent: Wednesday, May 23, 2018 9:06 PM
To: Keller, John W
Subject: CCL: RateDetermi= ning State

 

Anyao,

 

I refer you to the following two papers:

1) "Degree of Rate Control: How Much the E= nergies of Intermediates and Transition States Control Rates" (https://pubs.acs.org/do= i/abs/10.1021/ja9000097)

2) "Th= e Rate=E2=80=90Determining Step is Dead. Long Live the Rate=E2=80=90Determi= ning State!" (https://onlinelibrary.wiley.com/doi/full/10.1002/cphc.2= 01100137)

 

From a detailed mechanistic point of view, it is= often much more insightful to talk about about a rate-determining state (e= ither transition state or intermediate) than a rate-determining step. The f= irst paper describes the widely used definition of a rate-controlling state= and how to compute it. The second paper contains a few short examples of h= ow the rate-determining state differs from the rate-determining step.

 

On Wed, May 23, 2018 at 10:19 PM Anyao Jiao 1425623121(_)qq.com <o= wner-chemistry*ccl.net> wrote:


Sent to CCL by: "Anyao  Jiao" [1425623121^<= a href=3D"http://qq.com" target=3D"_blank">qq.com]
Dear All,
&nbs= p; I have a simple question regarding RateDetermining State. My reviewers <= br>suggest me to use RateDetermining State rather than Rate-Determining Ste= p
owning to its inaccuracy. Is that right? If we use the Canonical vari= ational
transition-state theory method, how do you calculate the reacti= on rate of the
RateDetermining State ?     
  &n= bsp;Sincerely

   Anyao Jiao



-=3D This is au= tomatically added to each message by the mailing script =3D-<br

<= br>E-mail to subscribers: CHEMISTRY*ccl.net or use:
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Before posting, check wait time a= t: http://www.ccl.net<= br>
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 <= /o:p>

= --_3AC69BCB-AFDB-4C0F-B2B6-D5397CA0C402_-- From owner-chemistry@ccl.net Thu May 24 04:35:01 2018 From: "Anyao Jiao 1425623121_._qq.com" To: CCL Subject: CCL: the rate of rate-determining state Message-Id: <-53316-180524035442-24650-aJeq1lbrZLMLtbyzuefOHA|-|server.ccl.net> X-Original-From: "Anyao Jiao" <1425623121]^[qq.com> Date: Thu, 24 May 2018 03:54:40 -0400 Sent to CCL by: "Anyao Jiao" [1425623121%x%qq.com] Dear Sir or Madam, Thank you very much for solving the doubts of rate-determining state. One question is how to phrase the rate of rate-determining state? Is the same with rate-determining step? Can I say " The theoretical rate coefficients for the rate-determining state calculated by the CVT and TST formalisms?" Sincerely Anyao Jiao School of Mechanical Engineering, Shanghai Jiao Tong University From owner-chemistry@ccl.net Thu May 24 08:09:00 2018 From: "Sebastian seb.kozuch]^[gmail.com" To: CCL Subject: CCL: the rate of rate-determining state Message-Id: <-53317-180524080544-15953-CX0q7wr/JbizyUv8f644pw:_:server.ccl.net> X-Original-From: Sebastian Content-Language: en-US Content-Transfer-Encoding: 8bit Content-Type: text/html; charset=utf-8 Date: Thu, 24 May 2018 15:06:00 +0300 MIME-Version: 1.0 Sent to CCL by: Sebastian [seb.kozuch]_[gmail.com] Dear Anyao,
You cannot freely say "rate determining state" instead of "rate determining step".  When you speak about states you mean one determining intermediate and one determining transition state, which may be in the same elementary step, but usually they don't. (Or you can consider all that section from the intermediate to the TS as one step as Per-Ola says. This was called the "rate determining zone"). You should check the literature about Campbell's degree of rate control and about the energy span model.

Regarding what John said:
"Rate limiting step... are still widely used in authoritative sources. Cramer’s Essentials… March’s Advanced Organic Chem... . Searching the titles of papers in JOC and J Phys. Chem 2012-1018, I found 11 that use “rate limiting” or “rate limiting step”, but no mention of “rate determining state”. Of course the latter phrase could be buried in journal texts."
The concept of rate determining states is rather new, and therefore it takes time to digest. I am very biased, but I believe that the concept of RDStates is correct, while RDSteps is not.

Best,
Sebastian


On 24/05/2018 10:54, Anyao Jiao 1425623121_._qq.com wrote:
Sent to CCL by: "Anyao  Jiao" [1425623121%x%qq.com]
Dear Sir or Madam,
      Thank you very much for solving the doubts of rate-determining state. One 
question is how to phrase the rate of rate-determining state? Is the same with 
rate-determining step? Can I say " The theoretical rate coefficients for the 
rate-determining state calculated by the CVT and TST formalisms?"
    Sincerely

   Anyao Jiao
School of Mechanical Engineering, Shanghai Jiao Tong UniversityE-mail to subscribers: CHEMISTRY###ccl.net or use:
      http://www.ccl.net/cgi-bin/ccl/send_ccl_message

E-mail to administrators: CHEMISTRY-REQUEST###ccl.net or use
      http://www.ccl.net/cgi-bin/ccl/send_ccl_messagehttp://www.ccl.net/chemistry/sub_unsub.shtml

Before posting, check wait time at: http://www.ccl.net

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Search Messages: http://www.ccl.net/chemistry/searchccl/index.shtmlhttp://www.ccl.net/spammers.txt

RTFI: http://www.ccl.net/chemistry/aboutccl/instructions/




-- 
═════════════════════════════════════
          Sebastian Kozuch
═════════════════════════════════════
       Department of Chemistry
 Ben-Gurion University of the Negev 
          Beer Sheva, Israel
           kozuch###bgu.ac.il
    http://www.bgu.ac.il/~kozuch
═════════════════════════════════════
From owner-chemistry@ccl.net Thu May 24 10:28:00 2018 From: "Thomas Exner thomas.exner]-[uni-konstanz.de" To: CCL Subject: CCL: eChemInfo workshop on drug design in Milano Message-Id: <-53318-180524074554-13616-7rtQ2Gr4JTnCcVrw8RVEAg- -server.ccl.net> X-Original-From: Thomas Exner Content-Transfer-Encoding: 8bit Content-Type: text/plain; charset=UTF-8; format=flowed Date: Thu, 24 May 2018 13:45:34 +0200 MIME-Version: 1.0 Sent to CCL by: Thomas Exner [thomas.exner##uni-konstanz.de] Dear all: Just a quick note that you can still profit from the early bird rates until the extended deadline May 31. Best. Thomas   Thomas Exner thomas.exner##douglasconnect.com schrieb: > Sent to CCL by: "Thomas Exner" [thomas.exner]_[douglasconnect.com] > Continuation of our workshop to resolve your problems in drug design > > Training and Innovation Course in Drug Design > 17-21 July 2018 > Department of Pharmaceutical Sciences, University of Milan > > Bring your own problems in rational drug design to this hands-on workshop co-organized by the University of Milans Department of Pharmaceutical Sciences and eChemInfo. > > Over one week, top modeling experts will teach you state-of-the-art/emerging approaches and tools and help you apply new knowledge to your own research. > > Real-world examples from academia and industry, as well as experimental techniques, will be strongly integrated into the workshop. Get personalized advice from Stefano Moro, University of Padova, working on G protein-coupled receptor ligand recognition pathways; Anthony Bradley from the Diamond Light Source's XChem project providing services for complex structure determination using medium- to high-throughput X-ray; and other industry experts soon to be announced. > > Participants - chemists, life scientists and modellers working in rational drug design - will return to their labs with new ideas, best practices and software experiences to maximise productivity in their own drug discovery research activities. > > http://www.echeminfo.com/events/echeminfo-euro-2018 > > The workshop will provide a set of stimulating lectures and group work sessions on: > - virtual screening, > - ligand-based and structure-based drug design, > - bio- and cheminformatics, > - molecular dynamics simulation, > - drug delivery modelling, > - off-target predictions and > - quantum mechanics in the hit-to-lead phase > > The functionalities of tools developed in academic groups and from the main software providers will be explored based on tutorials and, even more important, on case studies taken from ongoing research. > > Posters are welcome to give the other attendees an overview of your work and foster discussion within the groups. > > Bursary Awards are available; deadline extended until 30 April at http://www.echeminfo.com/bursary-awards > > Early-bird reduced rates are also available until 30 April. > > For further information and questions on this (updated program, exact location, suggestions for accommodation,...) and other eChemInfo workshops, please visit http://www.echeminfo.com/events or contact us. > Alessandro Contini, University of Milano (alessandro.contini%%unimi.it) > Thomas Exner, Douglas Connect (thomas.exner%%douglasconnect.com) > > P.S.: Please spread the word by showcasing the attached poster on your notice boards.> > -- ___________________________________________________________________________________ Dr. Thomas E. Exner Chief Scientific Officer Douglas Connect GmbH. www: www.douglasconnect.com e-mail: thomas.exner(-)douglasconnect.com phone: +49 (0)171 3807485 From owner-chemistry@ccl.net Thu May 24 12:01:00 2018 From: "Giuseppina Gini 10000654*polimi.it" To: CCL Subject: CCL: CFP special issue "Application of machine learning theories in QSAR/QSPR" - Int J of Quantitative Structure-Property Relationships (IJQSPR) Message-Id: <-53319-180524115234-31506-AvDr9vTG4KwALzPWWa6tVg++server.ccl.net> X-Original-From: Giuseppina Gini <10000654-.-polimi.it> Content-Type: multipart/alternative; boundary="Apple-Mail=_8B7F5388-05B9-4A34-AA07-6A8C32CE96AB" Date: Thu, 24 May 2018 17:52:06 +0200 Mime-Version: 1.0 (Mac OS X Mail 7.3 \(1878.6\)) Sent to CCL by: Giuseppina Gini [10000654() polimi.it] --Apple-Mail=_8B7F5388-05B9-4A34-AA07-6A8C32CE96AB Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=utf-8 This CfP can be of interest for people in the list. Apologies for = possible cross posting -=20 ********************* CALL FOR PAPERS ********************* SUBMISSION DUE DATE: October 1, 2018=20 SPECIAL ISSUE ON Application of machine learning theories in QSAR/QSPR International Journal of Quantitative Structure-Property Relationships = (IJQSPR) Guest Editor: Giuseppina Gini, DEIB, Politecnico di Milano, Italy INTRODUCTION: Many forces drive modern QSAR/QSPR models: - first of all the requirements of creating new chemicals with = wanted properties in fields as large as drugs, cosmetics, biocides, and = industrial products; - second, the growing concern about the risks of chemicals that = has produces advanced norms to regulate their use; - third, the availability of more data in the public domain that = opened the door to more advanced models; - finally, the tremendous improving of hardware and software that = allows more complex modeling, as represented in particular by the = development of Artificial Intelligence methods. About 20 years ago the Artificial Intelligence (AI) and the Toxicology = Communities joined together in the so-called =E2=80=9CPredictive = Challenge=E2=80=9D, where a few tens of molecules were given to = researchers to produce a QSAR model of cancerogenicity. The results then = showed that it is possible to build a QSAR model only using the chemical = structure. In the following years QSAR methods started to embrace new = learning methods, to incorporate more kinds of descriptors, and to move = > from linear to non-linear models. More recently, a new challenge promoted by the pharm industry was = launched in 2012 with thousands of chemical structures; among the many = models developed a deep neural net was found to be at the core of the = winning model. OBJECTIVE OF THE SPECIAL ISSUE: Even though Artificial Intelligence methods are commonly used in = modeling physical and biological properties of chemicals, there is a = lack of clearly assessing their advantages or disadvantages in building = QSAR/QSPR. Moreover, AI models may incorporate both symbolic and = implicit knowledge representations, and extracting and organizing = knowledge from such models is still challenging. This special issue will try to give answer to questions such as: =C2=B7 How AI based methods work in making accurate and predictive = QSAR/QSPR models? =C2=B7 What kind of knowledge is represented or hidden in AI-based = models? =C2=B7 Which kind of chemical and biological knowledge should and = could be given to AI based models? =C2=B7 How and why users can accept AI-based models? =C2=B7 What are the next challenges for QSAR/QSPR methods? =20 RECOMMENDED TOPICS: Topics to be discussed in this special issue include (but are not = limited to) the following:=20 Application of AI tools, including Support Vector Machines and Neural = Networks, to QSAR/QSPR Use of ensemble methods, as Random Forests, to build QSAR/QSPR Learning methods for modeling: pros and cons Deep Neural Nets and deep learning for building QSAR/QSPR Learning from chemical structures AI methods to define and choose descriptors AI methods to automatically extract relevant functional subgroups to = build SAR models AI methods to integrate SAR and QSAR Extracting knowledge from AI models of chemical/biological properties Interpreting AI models in terms of chemical and biological knowledge AI methods to integrate statistical results and expert knowledge AI tools to integrate in vivo and in vitro data Acceptance of AI methods in various user contexts Hardware and software to develop AI-based QSAR/QSPR models Theoretical developments of new QSARs using machine learning principles=20= SUBMISSION PROCEDURE: Researchers and practitioners are invited to submit papers for this = special theme issue on Application of machine learning theories in = QSAR/QSPR on or before October 1 2018.=20 All submissions must be original and may not be under review by another = publication. All submitted papers will be reviewed on a double-blind, = peer review basis. Papers must follow APA style for reference citations. = NO submission and publication FEES=E2=80=A8are asked for this journal.=20= All inquires should be directed to the attention of: Giuseppina Gini - Guest Editor - International Journal of Quantitative = Structure-Property Relationships (IJQSPR) E-mail: giuseppina.gini-*-polimi.it Giuseppina Gini DEIB, Politecnico di Milano piazza L. da Vinci 32 20133 Milano Italy --Apple-Mail=_8B7F5388-05B9-4A34-AA07-6A8C32CE96AB Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=utf-8

This CfP can be of interest for  people in = the list. Apologies for possible cross posting - 


********************* CALL FOR = PAPERS *********************

 SUBMISSION DUE DATE: October 1, = 2018 

SPECIAL ISSUE ON Application of machine learning theories = in QSAR/QSPR

International Journal of Quantitative Structure-Property = Relationships (IJQSPR)

 Guest = Editor: Giuseppina Gini, = DEIB, Politecnico di Milano, Italy


INTRODUCTION:

Many forces drive modern QSAR/QSPR = models:

-        first of all the = requirements of creating new chemicals with wanted properties in fields = as large as drugs, cosmetics, biocides, and industrial = products;

-        second, the growing = concern about the risks of chemicals that has produces advanced norms to = regulate their use;

-        third, the = availability of more data in the public domain that opened the door to = more advanced models;

-        finally, the = tremendous improving of hardware and software that allows more complex = modeling, as represented in particular by the development of Artificial = Intelligence methods.

About 20 years ago the Artificial Intelligence (AI) = and the Toxicology Communities joined together in the so-called = =E2=80=9CPredictive Challenge=E2=80=9D, where a few tens of molecules = were given to researchers to produce a QSAR model of cancerogenicity. = The results then showed that it is possible to build a QSAR model only = using the chemical structure. In the following years QSAR methods = started to embrace new learning methods, to incorporate more kinds of = descriptors, and to move from linear to non-linear = models.

More recently, a new = challenge promoted by the pharm industry was launched in 2012 with = thousands of chemical structures; among the many models developed a deep = neural net was found to be at the core of the winning = model.

 OBJECTIVE = OF THE SPECIAL ISSUE:

Even = though Artificial Intelligence methods are commonly used in modeling = physical and biological properties of chemicals, there is a lack of = clearly assessing their advantages or disadvantages in building = QSAR/QSPR. Moreover, AI models may incorporate both symbolic and = implicit knowledge representations, and extracting and organizing = knowledge from such models is still challenging.

This special issue will try to give answer to = questions such as:

=C2=B7       How AI based methods work = in making accurate and predictive QSAR/QSPR = models?

=C2=B7       What kind of knowledge is = represented or hidden in AI-based models?

=C2=B7       Which kind of chemical = and biological knowledge should and could be given to AI based = models?

=C2=B7       How and why users can = accept AI-based models?

=C2=B7       What are the next = challenges for QSAR/QSPR methods?

 

RECOMMENDED TOPICS:

Topics to be discussed in this special = issue include (but are not limited to) the following: 

  • Application of AI = tools, including Support Vector Machines and Neural Networks, to = QSAR/QSPR
  • Use of = ensemble methods, as Random Forests, to build = QSAR/QSPR
  • Learning = methods for modeling: pros and cons
  • Deep Neural Nets and deep learning for = building QSAR/QSPR
  • Learning from chemical = structures
  • AI = methods to define and choose descriptors
  • AI methods to automatically extract relevant = functional subgroups to build SAR models
  • AI methods to integrate SAR and = QSAR
  • Extracting knowledge from AI models of chemical/biological = properties
  • Interpreting AI models in terms of chemical and biological = knowledge
  • AI = methods to integrate statistical results and expert = knowledge
  • AI tools = to integrate in vivo and in vitro data
  • Acceptance of AI methods in various user = contexts
  • Hardware = and software to develop AI-based QSAR/QSPR = models
  • Theoretical developments of new QSARs using machine learning = principles 

SUBMISSION PROCEDURE:

Researchers and = practitioners are invited to submit papers for this special theme issue = on Application of machine learning theories in = QSAR/QSPR on or before October 1 = 2018. 

All submissions must be = original and may not be under review by another = publication. All submitted papers will be reviewed on a double-blind, peer = review basis. Papers must follow APA style for reference = citations. NO submission and publication FEES=E2=80=A8are asked for this = journal. 

 All inquires should be directed to the attention = of:

 Giuseppina Gini - Guest Editor - International Journal of Quantitative = Structure-Property Relationships (IJQSPR)

E-mail: giuseppina.gini-*-polimi.it

Giuseppina Gini
DEIB,  Politecnico di = Milano
piazza L. da Vinci 32
20133 Milano = Italy




= --Apple-Mail=_8B7F5388-05B9-4A34-AA07-6A8C32CE96AB-- From owner-chemistry@ccl.net Thu May 24 13:50:01 2018 From: "Vladimir Chupakhin chupvl]|[gmail.com" To: CCL Subject: CCL: CFP special issue "Application of machine learning theories in QSAR/QSPR" - Int J of Quantitative Structure-Property Relationships (IJQSPR) Message-Id: <-53320-180524132450-8384-hn6Mm5GcMEgzcs3TqPSQBQ*server.ccl.net> X-Original-From: Vladimir Chupakhin Content-Type: multipart/alternative; boundary="0000000000006aceab056cf6ede5" Date: Thu, 24 May 2018 13:24:04 -0400 MIME-Version: 1.0 Sent to CCL by: Vladimir Chupakhin [chupvl- -gmail.com] --0000000000006aceab056cf6ede5 Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Seriously, adv for predatory publishers? Best regards, Vladimir Chupakhin On Thu, May 24, 2018 at 11:52 AM, Giuseppina Gini 10000654*polimi.it < owner-chemistry-x-ccl.net> wrote: > This CfP can be of interest for people in the list. Apologies for > possible cross posting - > > > ********************* *CALL FOR PAPERS* ********************* > > SUBMISSION DUE DATE: *October 1, 2018* > > SPECIAL ISSUE ON *Application of machine learning theories in QSAR/QSPR* > > *International Journal of Quantitative Structure-Property Relationships > (IJQSPR)* > > Guest Editor: *Giuseppina Gini, DEIB, Politecnico di Milano, Italy* > > > INTRODUCTION: > > Many forces drive modern QSAR/QSPR models: > > - first of all the requirements of creating new chemicals with > wanted properties in fields as large as drugs, cosmetics, biocides, and > industrial products; > > - second, the growing concern about the risks of chemicals that > has produces advanced norms to regulate their use; > > - third, the availability of more data in the public domain that > opened the door to more advanced models; > > - finally, the tremendous improving of hardware and software that > allows more complex modeling, as represented in particular by the > development of Artificial Intelligence methods. > > About 20 years ago the Artificial Intelligence (AI) and the Toxicology > Communities joined together in the so-called =E2=80=9CPredictive Challeng= e=E2=80=9D, where > a few tens of molecules were given to researchers to produce a QSAR model > of cancerogenicity. The results then showed that it is possible to build = a > QSAR model only using the chemical structure. In the following years QSAR > methods started to embrace new learning methods, to incorporate more kind= s > of descriptors, and to move from linear to non-linear models. > > More recently, a new challenge promoted by the pharm industry was launche= d > in 2012 with thousands of chemical structures; among the many models > developed a deep neural net was found to be at the core of the winning > model. > > OBJECTIVE OF THE SPECIAL ISSUE: > > Even though Artificial Intelligence methods are commonly used in modeling > physical and biological properties of chemicals, there is a lack of clear= ly > assessing their advantages or disadvantages in building QSAR/QSPR. > Moreover, AI models may incorporate both symbolic and implicit knowledge > representations, and extracting and organizing knowledge from such models > is still challenging. > > This special issue will try to give answer to questions such as: > > =C2=B7 How AI based methods work in making accurate and predictive > QSAR/QSPR models? > > =C2=B7 What kind of knowledge is represented or hidden in AI-based > models? > > =C2=B7 Which kind of chemical and biological knowledge should and c= ould > be given to AI based models? > > =C2=B7 How and why users can accept AI-based models? > > =C2=B7 What are the next challenges for QSAR/QSPR methods? > > > > RECOMMENDED TOPICS: > > Topics to be discussed in this special issue include (but are not limited > to) the following: > > - *Application of AI tools, including Support Vector Machines and > Neural Networks, to QSAR/QSPR* > - *Use of ensemble methods, as Random Forests, to build QSAR/QSPR* > - *Learning methods for modeling: pros and cons* > - *Deep Neural Nets and deep learning for building QSAR/QSPR* > - *Learning from chemical structures* > - *AI methods to define and choose descriptors* > - *AI methods to automatically extract relevant functional subgroups > to build SAR models* > - *AI methods to integrate SAR and QSAR* > - *Extracting knowledge from AI models of chemical/biological > properties* > - *Interpreting AI models in terms of chemical and biological > knowledge* > - *AI methods to integrate statistical results and expert knowledge* > - *AI tools to integrate in vivo and in vitro data* > - *Acceptance of AI methods in various user contexts* > - *Hardware and software to develop AI-based QSAR/QSPR models* > - *Theoretical developments of new QSARs using machine learning > principles* > > SUBMISSION PROCEDURE: > > Researchers and practitioners are invited to submit papers for this > special theme issue on *Application of machine learning theories in > QSAR/QSPR* *on or before* *October 1 2018*. > > All submissions must be original and may not be under review by another > publication. All submitted papers will be reviewed on a double-blind, > peer review basis. Papers must follow APA style for reference citations. = NO > submission and publication FEES are asked for this journal. > All inquires should be directed to the attention of: > > *Giuseppina Gini - *Guest Editor - *International Journal of > Quantitative Structure-Property Relationships (IJQSPR)* > > E-mail: *giuseppina.gini~!~polimi.it * > Giuseppina Gini > DEIB, Politecnico di Milano > piazza L. da Vinci 32 > 20133 Milano Italy > > > > > --0000000000006aceab056cf6ede5 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Seriously, adv for predatory publishers?

Best regards,
Vladimir Chupakhin

On Thu, May 24, 2018 at 11:52 AM, Gi= useppina Gini 10000654*polimi.it <o= wner-chemistry-x-ccl.net> wrote:

Th= is CfP can be of interest for =C2=A0people in the list. Apologies for possi= ble cross posting -=C2=A0


*********************=C2=A0C= ALL FOR PAPERS=C2=A0*********************

=C2=A0SUBMISSION DUE DATE:=C2=A0October 1, 20= 18=C2=A0

SPECIAL ISSUE ON=C2=A0Application of machine learning = theories in QSAR/QSPR

International Journal of Quantitative = Structure-Property Relationships (IJQSPR)

=C2=A0Guest Editor:= =C2=A0Giuseppina Gini, DEIB, Polite= cnico di Milano, Italy


INTRODUCTION:

Many = forces drive modern QSAR/QSPR models:

-=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0first of all the requirements of creating new chemicals with wanted= properties in fields as large as drugs, cosmetics, biocides, and industria= l products;

-=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0second, the g= rowing concern about the risks of chemicals that has produces advanced norm= s to regulate their use;

-=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0third, the availability of more data in the public domain that opened the= door to more advanced models;

-=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0finally, the tremendous improving of hardware and software that all= ows more complex modeling, as represented in particular by the development = of Artificial Intelligence methods.

About 20 years ago the Artificial Intelligence (AI= ) and the Toxicology Communities joined together in the so-called =E2=80=9C= Predictive Challenge=E2=80=9D, where a few tens of molecules were given to = researchers to produce a QSAR model of cancerogenicity. The results then sh= owed that it is possible to build a QSAR model only using the chemical stru= cture. In the following years QSAR methods started to embrace new learning = methods, to incorporate more kinds of descriptors, and to move from linear = to non-linear models.

More recently, a new challenge promoted by the pharm industry was launc= hed in 2012 with thousands of chemical structures; among the many models de= veloped a deep neural net was found to be at the core of the winning model.=

= =C2=A0OBJECTIVE OF THE SPECIAL I= SSUE:

Even though Artific= ial Intelligence methods are commonly used in modeling physical and biologi= cal properties of chemicals, there is a lack of clearly assessing their adv= antages or disadvantages in building QSAR/QSPR. Moreover, AI models may inc= orporate both symbolic and implicit knowledge representations, and extracti= ng and organizing knowledge from such models is still challenging.

This special issue will tr= y to give answer to questions such as:

=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0How AI based methods work in making accurate and predictive QSAR/QSPR = models?

=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0<= /span>What kind of knowl= edge is represented or hidden in AI-based models?

= =C2=B7=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0Which kind of chemical and biological knowledge sh= ould and could be given to AI based models?

=

= =C2=B7=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0How and why users can accept AI-based models?

=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0What are the next challenges for QSAR/Q= SPR methods?

=C2=A0

REC= OMMENDED TOPICS:

Topi= cs to be discussed in this special issue include (but are not limited to) t= he following:=C2=A0

  • = Applicati= on of AI tools, including Support Vector Machines and Neural Networks, to Q= SAR/QSPR
  • Use of ensemble met= hods, as Random Forests, to build QSAR/QSPR
    • Learning methods for modeling: pros and cons<= /span>
  • Deep Neural Nets and deep learning for bui= lding QSAR/QSPR
  • Learning fro= m chemical structures
  • = AI met= hods to define and choose descriptors
  • AI methods to automatically extract relevant functional subgroups= to build SAR models
  • <= b>AI meth= ods to integrate SAR and QSAR
  • Extracting knowledge from AI models of chemical/biological properties<= /u>
  • Interpreting AI models in terms= of chemical and biological knowledge
  • AI methods to integrate statistical results and expert knowledge<= u>
  • AI tools to integrate in viv= o and in vitro data
  • Acceptan= ce of AI methods in various user contexts
  • Hardware and software to develop AI-based QSAR/QSPR models=
  • Theoretical de= velopments of new QSARs using machine learning principles=C2=A0

SUBMISSION PROCEDURE:

Rese= archers and practitioners are invited to submit papers for this special the= me issue on=C2=A0Application of machine learning theories in QSAR/QSPR=C2=A0on or before= =C2=A0October 1 2018.=C2=A0

All submis= sions must be original and may not be under review by another publication.= =C2=A0All submitt= ed papers will be reviewed on a double-blind, peer review basis. Papers mus= t follow APA style for reference citations.=C2=A0NO submission and publication FEES=E2=80=A8are asked for thi= s journal.=C2=A0<= u>

=C2=A0All inquires should be directed to = the attention of:

=C2=A0Giuseppina Gini -=C2=A0G= uest Editor -=C2=A0International Jour= nal of Quantitative Structure-Property Relationships (IJQSPR)

E-mail:=C2=A0giuseppina.gini~!~polimi.it

=
Giuseppina Gini
DEIB, =C2=A0Po= litecnico di Milano
piazza L. da Vinci 32
20133 Milano Italy





--0000000000006aceab056cf6ede5-- From owner-chemistry@ccl.net Thu May 24 21:03:01 2018 From: "Kunal Roy kunalroy_in###yahoo.com" To: CCL Subject: CCL: CFP special issue "Application of machine learning theories in QSAR/QSPR" - Int J of Quantitative Structure-Property Relationships (IJQSPR) Message-Id: <-53321-180524204630-24631-EOhzIwaPbd+YgeBPd5zwhQ:server.ccl.net> X-Original-From: Kunal Roy Content-Type: multipart/alternative; boundary="----=_Part_5316733_945083801.1527209169577" Date: Fri, 25 May 2018 00:46:09 +0000 (UTC) MIME-Version: 1.0 Sent to CCL by: Kunal Roy [kunalroy_in+/-yahoo.com] ------=_Part_5316733_945083801.1527209169577 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable There is no submission or processing fee for this journal (https://www.igi-= global.com/journal/international-journal-quantitative-structure-property/12= 6552=C2=A0) ! [And this Publisher has never been in the list of predatory publishers as a= ll their journals are subscription based] Prof. Kunal Roy, Ph.D.=20 Professor, Drug Theoretics and Cheminformatics Lab, Department of Pharmaceu= tical Technology,=20 JADAVPUR UNIVERSITY, Kolkata 700 032 (INDIA)Email : kroy(_)pharma.jdvu.ac.in = , kunalroy_in(_)yahoo.com=C2=A0=C2=A0; Phone: +91 33 2457 2051 (Off)URL :=C2= =A0=C2=A0 http://sites.google.com/site/kunalroyindia/,=C2=A0http://www.jadu= niv.edu.in/profile.php?uid=3D550Publication list:=C2=A0http://www.researche= rid.com/rid/B-1673-2009=C2=A0=C2=A0ORCID:=C2=A0http://orcid.org/0000-0003-4= 486-8074Formerly, Marie Curie International Incoming Fellow and Commonwealt= h Academic Staff Fellow, University of Manchester, UK =20 On Friday, 25 May, 2018, 12:37:55 AM IST, Vladimir Chupakhin chupvl]|[g= mail.com wrote: =20 =20 Seriously, adv for predatory publishers? Best regards,Vladimir Chupakhin On Thu, May 24, 2018 at 11:52 AM, Giuseppina Gini 10000654*polimi.it wrote: This CfP can be of interest for =C2=A0people in the list. Apologies for pos= sible cross posting -=C2=A0 *********************=C2=A0CALL FOR PAPERS=C2=A0********************* =C2=A0SUBMISSION DUE DATE:=C2=A0October 1, 2018=C2=A0 SPECIAL ISSUE ON=C2=A0Application of machine learning theories in QSAR/QSPR International Journal of Quantitative Structure-Property Relationships (IJQ= SPR) =C2=A0Guest Editor:=C2=A0Giuseppina Gini, DEIB, Politecnico di Milano, Ital= y INTRODUCTION: Many forces drive modern QSAR/QSPR models: -=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0first of all the requireme= nts of creating new chemicals with wanted properties in fields as large as = drugs, cosmetics, biocides, and industrial products; -=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0second, the growing concer= n about the risks of chemicals that has produces advanced norms to regulate= their use; -=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0third, the availability of= more data in the public domain that opened the door to more advanced model= s; -=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0finally, the tremendous im= proving of hardware and software that allows more complex modeling, as repr= esented in particular by the development of Artificial Intelligence methods= . About 20 years ago the Artificial Intelligence (AI) and the Toxicology Comm= unities joined together in the so-called =E2=80=9CPredictive Challenge=E2= =80=9D, where a few tens of molecules were given to researchers to produce = a QSAR model of cancerogenicity. The results then showed that it is possibl= e to build a QSAR model only using the chemical structure. In the following= years QSAR methods started to embrace new learning methods, to incorporate= more kinds of descriptors, and to move from linear to non-linear models. More recently, a new challenge promoted by the pharm industry was launched = in 2012 with thousands of chemical structures; among the many models develo= ped a deep neural net was found to be at the core of the winning model. =C2=A0OBJECTIVE OF THE SPECIAL ISSUE: Even though Artificial Intelligence methods are commonly used in modeling p= hysical and biological properties of chemicals, there is a lack of clearly = assessing their advantages or disadvantages in building QSAR/QSPR. Moreover= , AI models may incorporate both symbolic and implicit knowledge representa= tions, and extracting and organizing knowledge from such models is still ch= allenging. This special issue will try to give answer to questions such as: =C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0How AI based methods work i= n making accurate and predictive QSAR/QSPR models? =C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0What kind of knowledge is r= epresented or hidden in AI-based models? =C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0Which kind of chemical and = biological knowledge should and could be given to AI based models? =C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0How and why users can accep= t AI-based models? =C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0What are the next challenge= s for QSAR/QSPR methods? =C2=A0 RECOMMENDED TOPICS: Topics to be discussed in this special issue include (but are not limited t= o) the following:=C2=A0 =20 - Application of AI tools, including Support Vector Machines and Neural = Networks, to QSAR/QSPR - Use of ensemble methods, as Random Forests, to build QSAR/QSPR - Learning methods for modeling: pros and cons - Deep Neural Nets and deep learning for building QSAR/QSPR - Learning from chemical structures - AI methods to define and choose descriptors - AI methods to automatically extract relevant functional subgroups to b= uild SAR models - AI methods to integrate SAR and QSAR - Extracting knowledge from AI models of chemical/biological properties - Interpreting AI models in terms of chemical and biological knowledge - AI methods to integrate statistical results and expert knowledge - AI tools to integrate in vivo and in vitro data - Acceptance of AI methods in various user contexts - Hardware and software to develop AI-based QSAR/QSPR models - Theoretical developments of new QSARs using machine learning principle= s=C2=A0 SUBMISSION PROCEDURE: Researchers and practitioners are invited to submit papers for this special= theme issue on=C2=A0Application of machine learning theories in QSAR/QSPR= =C2=A0on or before=C2=A0October 1 2018.=C2=A0 All submissions must be original and may not be under review by another pub= lication.=C2=A0All submitted papers will be reviewed on a double-blind, pee= r review basis. Papers must follow APA style for reference citations.=C2=A0= NO submission and publication FEES=E2=80=A8are asked for this journal.=C2= =A0 =C2=A0All inquires should be directed to the attention of: =C2=A0Giuseppina Gini -=C2=A0Guest Editor -=C2=A0International Journal of Q= uantitative Structure-Property Relationships (IJQSPR) E-mail:=C2=A0giuseppina.gini~!~ polimi.it Giuseppina Gini DEIB, =C2=A0Politecnico di Milanopiazza L. da Vinci 32 20133 Milano Italy =20 ------=_Part_5316733_945083801.1527209169577 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
There is no submission or processing fee for = this journal (https://www.igi-global.com/journal/international-journal-quantitativ= e-structure-property/126552 ) !

[And this= Publisher has never been in the list of predatory publishers as all their = journals are subscription based]


Prof. Kunal= Roy, Ph.D.
Pro= fessor, Drug Theoretics and Cheminformatics Lab, Department of Pharmaceutic= al Technology,
JADAVPUR UNIVERSITY, Kolkata 700 032 (INDI= A)
Publication list: http://www= .researcherid.com/rid/B-1673-2009  
<= b style=3D"text-align:justify;line-height:21px;background-color:rgb(255, 25= 5, 255);">ORCID: http://orcid.org/000= 0-0003-4486-8074
Formerly, Marie Curie International Incoming Fellow and C= ommonwealth Academic Staff Fellow, University of Manchester, UK




=20
=20
On Friday, 25 May, 2018, 12:37:55 AM IST, Vladimir = Chupakhin chupvl]|[gmail.com <owner-chemistry(_)ccl.net> wrote:


Serious= ly, adv for predatory publishers?

Best regards,
Vladimir Chupakhin
On Thu, May 24, 2018 at 11:52 AM, = Giuseppina Gini 10000654*polimi.it <owner-chemistry[]ccl.net> wrote:

This CfP can be of inte= rest for  people in the list. Apologies for possible cross posting -&n= bsp;


<= /p>

*********************&nbs= p;CALL FOR PAPERS *********************

 SUBMISSION DUE DATE:=  October 1, 2018&nb= sp;

SPECIAL I= SSUE ON Application of machine learning theories in QSAR/QSPR<= span lang=3D"EN-US" style=3D"font-family:Candara;">

International Journal of Quantitative Structure-Property R= elationships (IJQSPR)

 Guest Editor: = Giuseppina Gini, DEIB, Politecnico= di Milano, Italy


=

INTRODUCTION:

Many forces drive modern QSAR/QSPR models:<= u>

-  &nb= sp;     first of all the requirements of creating new che= micals with wanted properties in fields as large as drugs, cosmetics, bioci= des, and industrial products;

-        second, the gro= wing concern about the risks of chemicals that has produces advanced norms = to regulate their use;

-<= span style=3D"">        third, the availabil= ity of more data in the public domain that opened the door to more advanced= models;

-=         finally, the tremendous improving of= hardware and software that allows more complex modeling, as represented in= particular by the development of Artificial Intelligence methods.

About= 20 years ago the Artificial Intelligence (AI) and the Toxicology Communiti= es joined together in the so-called =E2=80=9CPredictive Challenge=E2=80=9D,= where a few tens of molecules were given to researchers to produce a QSAR = model of cancerogenicity. The results then showed that it is possible to bu= ild a QSAR model only using the chemical structure. In the following years = QSAR methods started to embrace new learning methods, to incorporate more k= inds of descriptors, and to move from linear to non-linear models.

More recentl= y, a new challenge promoted by the pharm industry was launched in 2012 with= thousands of chemical structures; among the many models developed a deep n= eural net was found to be at the core of the winning model.

&n= bsp;OBJECTIVE OF THE SPECIAL IS= SUE:

Even t= hough Artificial Intelligence methods are commonly used in modeling physica= l and biological properties of chemicals, there is a lack of clearly assess= ing their advantages or disadvantages in building QSAR/QSPR. Moreover, AI m= odels may incorporate both symbolic and implicit knowledge representations,= and extracting and organizing knowledge from such models is still challeng= ing.

This special issue will try to give answer to questions such as:=

=C2=B7   &nb= sp;   How AI based methods work in making accurate and predictive QSA= R/QSPR models?

=C2=B7       What kind of knowledge is repres= ented or hidden in AI-based models?

= =C2=B7       Which kind = of chemical and biological knowledge should and could be given to AI based = models?

=C2=B7       How and why users can accept AI-based m= odels?

=C2=B7       What are the next challenges for QSAR/QS= PR methods?

 

RECOMMENDED TOPICS:

Topics to be discussed in this speci= al issue include (but are not limited to) the following: 

  • Application of AI tool= s, including Support Vector Machines and Neural Networks, to QSAR/QSPR
  • Use of ensemble me= thods, as Random Forests, to build QSAR/QSPR
    • <= li class=3D"yiv6145120237MsoNormal">Learning methods for modeling: pros and cons=
  • Deep Neural N= ets and deep learning for building QSAR/QSPR
    • <= li class=3D"yiv6145120237MsoNormal">Learning from chemical structures<= /u>
  • AI methods to define and= choose descriptors
  • AI methods to automatically extract relevant functional subgroups to = build SAR models
  • AI methods to integrate SAR and QSAR
  • Extracting knowledge from AI models of chemical/b= iological properties
  • Interpreting AI models in terms of chemical and biological knowledge=
  • AI methods to= integrate statistical results and expert knowledge
  • AI tools to integrate in vivo and in = vitro data
  • Acc= eptance of AI methods in various user contexts
  • Hardware and software to develop AI-based = QSAR/QSPR models
  • Theoretical developments of new QSARs using machine lea= rning principles 

<= span lang=3D"EN-US" style=3D"font-family:Candara;font-size:11px;">SUBMISSIO= N PROCEDURE:

Researchers and = practitioners are invited to submit papers for this special theme issue on&= nbsp;Applicat= ion of machine learning theories in QSAR/QSPR on or before <= /i>October 1 2018. 

Al= l submissions must be original and may not be under review by another publi= cation. All= submitted papers will be reviewed on a double-blind, peer review basis. Pa= pers must follow APA style for reference citations. NO submission and publication FEE= S=E2=80=A8are as= ked for this journal. 

 All inquires should b= e directed to the attention of:

 Giuseppina Gini - Guest Editor - International Journal of Quantitative Structure-Property = Relationships (IJQSPR)

E-mail: giuseppina.gini~!= ~ polimi.it

Giusep= pina Gini
DEIB,  Politecnico di Milano
piazza L. da Vinci 3220133 Milano Italy




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