Discover and read the best of Twitter Threads about #11thiccs
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#11thICCS Andreas Goller on Anisotropic atom react descs for the pred of liver metabolism, ames tox and H bonding
Refers to DFTB+ for calculation. Check out github.com/dftbplus and dftbplus.org - another open source QM package I'd never heard of.
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#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.
The molecular pattern comparison problem. Difficult to compare patterns. Are two patterns the same? One may match a subset of the others.
#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.
Has 53-page CV which Val is going to try to condense. 578 publications incl. 16 books. Well-known in bibliometrics and info retrieval (not just cheminf).
#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.
Treatment -> transcvriptomics signature -> phenotypic readout. The signature gives additional insight that may be useful to treat the disease.
#11thICCS Modest van Korff on Targeting of the disease-related proteome by small molecules
The disease is the phenotype - what you observe in the human. It's outside the normal conditions. MeSH terms cover 4500 diseases. A disease has to be severe and no sufficient treatment available for us to start working on a new drug.
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#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.
Instead could base it on a pathway
Has been v successful in the past. 28 out of 50 first in class FDA approved small mols have come from this.
Has been v successful in the past. 28 out of 50 first in class FDA approved small mols have come from this.
#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.
The proximal Lilly collection (PLC). Given our cmpd collection, how can we create a database to cover chemical space more fully. Virtual syn engine-->PLC--->in silico design and selection. "Old news"
#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.
Looking first at substructure searching. Use of binary fps to prescreen possible matches improves perf for typical queries. However, the use of fps does not affect the worse case, e.g. [X5], will bring many search systems to their knees.
#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.
The applicability of PAINS is limited. (P Kenny referenced)
#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.
Some strategies in FBDD. Fragment linking, merging and growing - these are diff approaches. Growing is the most popular. Shows example of this from Astex (Murray ACS MedChemLett 2015, 6, 798)
#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.
On average drugs hit six protein targets in the body, but designed to hit just one. Oncology they try to hit multiple targets within one disease type to avoid resistance. 3.4% success rate of drug in oncology.
#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.
Starts via micro injuries. Death of eopithelial cells, and then abnormal healing process leading to a cycle of repetitive lung remodelling.
#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.
N-myristoyltransferase (NMT). Co-and post-translational mods of proteins for membrane targeting. Target for cancer and African sleeping sickness.
#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.
MD vs torsion sampling. Slow versus fast. Distance geometry: fast, don't require 3d, stochastic, implicit solvent.
#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.
UNC 119A was the target. No inhibitor was known at the start. An unexplored protein target. Was able to do unbiased assessment - no prior knowledge.
#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).
A non-causal example would be one based entirely on the molecular weight.
#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.
As Astex, PLIff scoring function used a lot in VS. Knowledge-based using info from PDB. Voronoi partitioning to calculate solvent accessible areas, contact areas, contact geometries.
#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.
QM-derived partial charges. Extraction from electron density is an undetermined maths problem. Most try to fit to ESP with Kollman-Singh, semi-empirical with bond order corrections, e.g. AM1-BCC. Issues, low quality QM (decrease cost), conformational dependence.
#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
CRISP-DM - standard process for data mining solutions - see wikipedia
#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.
Including heterogenous data can improve performance of tox models. Wanted to repeat approach of Alex Tropsha on a larger dataset.
#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
Reduced graphs represented as SMILES where particular features are represented using unusual elements, e.g. [Sc] for aromatic.
#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.
Building blocks (Sigma) + transforms (LHASA) + cheminformatics engine (Xemistry) = SAVI.
