#11thICCS Robert Schmidt on Comparison and Analysis of Molecular Patterns on the Example of SMARTS Gives example: [O,N]-!@c(:c):[a!c] and visualization via Smarts Viewer. Red indicates negation - matches everything except whatever.

#11thICCS Cheminformatics session dedicated to Peter Willett (who is retiring). Val Gillet corrects this. He is *not* retiring, but reached a 40 year milestone of contribs to cheminf. Peter cannot make it today due to family reasons.

#11thICCS Natalia Aniceto on Gearing transcriptomics towards HTS: Cmpd shortlisting from gene expression using in silico information Gene expression provides additional insight beyond phenotype into biological processes, which can be used to find new drugs. Original paradigm: focussing on single target. But current paradigm is to use systems biology to look at a set of genes.

#11thICCS Henriëtte Willems on Strategies for assembling an annotated library for phenotypic screening Phenotypic screening is an alternative to target-based drug discovery. Could have high-content imaging screen, find phenotypic hits, then find the target/biology.

#11thICCS Christos Nicolaou on Advancing automated syn via rxn data mining and reuse The automated synthesis and purification labs (ASL and ASP). "Pretty cool robots". Scientists come up with workflows, click a button and submit, and if all goes well you get the end product at the other side.

#11thICCS Roger Sayle of @nmsoftware on Recent advances in chemical & biological search systems: Evolution vs. revolution Databases are growing at rates exceeding Moore's Law. Dbs that are twice as big take twice as long. But sublinear methods will not slow down that fast as dbs get bigger. At 1M mol/s searching, ChemEBL in 2s, PubChem 1.5min, Enamine REAL in 10mins.

#11thICCS Johannes Kirchmair Hit Dexter 2.0: machine learning for triaging hits from biochemical assays People are still taking about PAINS. The editors of various med chem journals teamed up to describe how to id false positive hits and reject them. J. Med. Chem. were the 1st to adopt PAINS as a decision framework to id cmpds that should be tested in detail.

#11thICCS Moira Michelle Rachman on Automated frag evolution (FrEvolAted) applied to frags bound to NUDT21 Fragment chemical space is a lot smaller and easily to explore a larger chemical space more efficiently. Smaller and so more likely to bind, higher hit rates, more chemical tractable.

#11thICCS Willem Jespers on behalf of EB Lenselink on Lessons learned in benchmarking VS for polypharmacology VS has been quite successful - hit rates often exceed 10%. DB sizes getting quite big. ZINC 800M, Enamine REAL > 300M.

#11thICCS Steven Oatley on Active search for computer-aided drug design The disease we are working on is Idiopathic pulmonary fibrosis (IPF) - progressive lung disease. Scar tissue in the lungs. Shortness of breath, dry cough. Often fatal within 2-5 years. Worse for smokers. Genetic factor.

#11thICCS Ruth Brenk on selectivity determining features in proteins with conserved binding sites How to rationally design selective inhibitors: steric clash, electrostatic interactions, allostery, flexibility, hydration (Huggins et al 2012). The more conserved it is, the harder it'll likely be.

#11thICCS Paul Hawkins on conformational sampling macrocycles in solution and in the solid state The iron triangle in conformation generation: want fast, good and cheap; i.e. fast, accurate and small ensemble.

#11thICCS Oliver Koch on an exhaustive assessment of computer-based drug discovery methods by HTS data We were challenged to test our methods against HTS results. Not a simple benchmarking exercise, but a real life task.

#11thICCS Jochen Sieg taking about bias control in structure-based virtual screening with machine learning When you build a model in SBVS, is the predictor really generalizing? High correlation does not imply causation. We want to distinguish between patterns in the data we want to learn (causal patterns) and those we don't want to learn (non-causal).

#11thiccs Prakash Chandra Rathi on AI for predicting molecular ESPs ESPs v. useful for optimising lead cmpds. Shows example with electrostatic clash. Changed structure to pull electrons away from pi cloud. Much better binding.

#11thiccs Sereina Riniker on Machine learning of partial charges from QM calcs and the applic in fixed-charge force fields and cheminf A classical fixed-charge force field has parameters for bonded atoms and non-bonded. The non-bonded are most important for interactions. Review by me in JCIM 2018, 58, 565. Bonded are from crystallography. Charges come from QM, fitted to liquid properties.

#11thICCS Greg Landrum on How do you build and validate 1500 models and what can you learn from them? Really..."the Monster Model Factory".. Have >1500 datasets from CHEMBL that I want to build models for. Needs to be automated. Ideally we can learn s.t. about what makes model work vs not work

#11thICCS Chad Allen on the Analysis of ToxCast & Tox21 cmpd set using GHS toxicity annotations and in-silico derived protein-target descriptors Motivation: the need for tox data outstrips the output of traditional toxicology. In-silico methods can help.

#11thICCS Peter Pogany - Fast molecular searching tools and their extension at GSK Search tools at GSK: Uses MadFast from Chemaxon; SmallWorld from NextMove; FFSS from GSK; Fraggle from GSK

#11thICCS Hitesh Patel presents SAVI - Synthetically accessible virtual inventory Q: What can I make easily, reliably, safely and cheaply? Make a db of 1 billion mols where you know that this is the case. 1-step rxns. Freely available db.