From owner-chemistry@ccl.net Wed Oct 3 03:26:01 2018 From: "Simmie, John john.simmie#%#nuigalway.ie" To: CCL Subject: CCL: Relative Stability of species. 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CCL Subject: CCL: NN rigor? Message-Id: <-53496-181003051843-30611-f6F6Nns8kO2ie2arLFSWCA=-=server.ccl.net> X-Original-From: Michael Sluydts Content-Language: nl Content-Transfer-Encoding: 8bit Content-Type: text/plain; charset=utf-8; format=flowed Date: Wed, 3 Oct 2018 11:18:33 +0200 MIME-Version: 1.0 Sent to CCL by: Michael Sluydts [michael.sluydts]*[ugent.be] Hey, It's machine learning so if it works, so if it works you can say it works for that dataset.  The methodology itself is okay, it's a fairly simple network, but more a classical feed-forward neural network than 'deep' learning. The important question here is always how well it generalizes to new data. When training such a model you should split the dataset into a train, validation and test set. The training set is used to fit the model and can thus be perfectly memorized, the validation set is meant to give an idea of generalization during training. The more configurations of the model you try the more likely you will also start fitting the validation set as you bias your judgement of what is a good model towards validation performance. Ideally the final model should be tested on unseen data for this reason. The paper here mentions a validation set, though during my quick skim of the paper I didn't see how big or different this set was from the training data (ideally both different and similar enough to represent the data you want to use the model on). I did not see any mention of a test set. Likely, applying the model on your own data should be fine (or reproducing this model), but a good training procedure is essential to have a model which is also practically useful. Kind regards, Michael Op 2/10/2018 om 18:28 schreef Soaring Bear soaringbear]^[yahoo.com: > Sent to CCL by: "Soaring Bear" [soaringbear:+:yahoo.com] > to NN experts, I wonder if this seems rigorous to you? > > deep neural network (DNN) model that had only 2000 hidden units. > After forward propagation, the feature dimensionality was > reduced approximately 200 times. By the conclusion of > training, the DNN model derived a decision boundary > to classify positive and negative samples with the desired > accuracy, and the model was able to predict reliable DTIs. > > > ref: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156897/> > -- dr. ir. Michael Sluydts Center for Molecular Modeling Ghent University Technologiepark 903 9052 Zwijnaarde, Belgium tel. +32 (0)9 264 66 19 https://molmod.ugent.be From owner-chemistry@ccl.net Wed Oct 3 16:07:00 2018 From: "Jan Jensen compchemhighlights%gmail.com" To: CCL Subject: CCL: Computational Chemistry Highlight: September issue Message-Id: <-53497-181003064347-7050-WRm2YYiWJMwmphgBQh3pkQ..server.ccl.net> X-Original-From: Jan Jensen Content-Type: multipart/alternative; boundary="000000000000420986057750b607" Date: Wed, 3 Oct 2018 12:43:31 +0200 MIME-Version: 1.0 Sent to CCL by: Jan Jensen [compchemhighlights:_:gmail.com] --000000000000420986057750b607 Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable The September issue of Computational Chemistry Highlights is out. CCH is an overlay journal th= at identifies the most important papers in computational and theoretical chemistry published in the last 1-2 years. CCH is not affiliated with any publisher: it is a free resource run by scientists for scientists. You can read more about it here . Table of content for this issue features contributions from Steven Bachrach and Jan Jensen: DeepSMILES: An adaptation of SMILES for use in machine-learning of chemical structures Curved Aromatic molecules =E2=80=93 4 new examples Rearrangement of Hydroxylated Pinene Derivatives to Fenchone-Type Frameworks: Computational Evidence for Dynamically-Controlled Selectivity Interested in contributing? Read more here Interested in more? There are many ways to subscribe to CCH updates . --000000000000420986057750b607 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable

The Septem= ber issue of=C2=A0Computational Chemistry Highlights=C2=A0is out.


CCH is an=C2= =A0overlay journal= =C2=A0that identifies the most important papers in computational and th= eoretical chemistry published in the last 1-2 years. CCH is not affiliated = with any publisher: it is a free resource run by scientists for scientists.= =C2=A0You can read more about it here= .


= Table of content for this issue features contributions from Steven Bachrach= and Jan Jensen:


DeepSMILES: An adaptation of SMILES = for use in machine-learning of chemical structures

=


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