How IJC is Adding Value to a Molecular Design Business
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1 How IJC is Adding Value to a Molecular Design Business James Mills Sexis LLP ChemAxon TechTalk Stevenage, ov 2012 james.mills@sexis.co.uk
2 Overview Introduction to Sexis Sexis IJC use cases Data visualisation Post-processing of virtual screening Monomer selection for library design PDB lig database Future directions
3 Sexis LLP Partnership of experienced PhD medicinal computational chemists Provide variety of flexible bespoke medicinal computational chemistry services to biotech, pharma, CRO not-for-profit drug discovery sectors: Medicinal computational chemistry design support Management of integrated drug discovery projects from hit generation through to cidate identification including outsourced synthetic chemistry Generation optimisation of intellectual property Consultancy for due diligence, project reviews, proposal advice, problem solving, expert witness, grant writing literature searches Bespoke computational chemistry solutions Design delivery of medicinal computational chemistry training
4 Sexis IJC ovice users (6 months) Only explored made use of basic functionality But have still garnered added value Translate Comp Chem geekery Set up IJC databases for each piece of work into means for Med Chem decision-making Access via password protected files on cloud Variety of use cases SAR analysis Library Design Virtual Screening PDB lig database
5 SAR analysis Analysis of Lipophilic lig efficiency. Scatter plot sized by MW, coloured by TPSA Simple use case calculated properties of data to afford pec50 values LipE (pec50 ClogP). Histogram analysis by LipE allows simple selection analysis of efficient ligs all within IJC.
6 SAR analysis Analysis of Lipophilic lig efficiency. Scatter plot sized by MW, coloured by TPSA Simple use case calculated properties of data to afford pec50 values LipE (pec50 ClogP). Histogram analysis by LipE allows simple selection analysis of efficient ligs all within IJC.
7 SAR analysis Analysis of Lipophilic lig efficiency. Scatter plot sized by MW, coloured by TPSA Simple use case calculated properties of data to afford pec50 values LipE (pec50 ClogP). Histogram analysis by LipE allows simple selection analysis of efficient ligs all within IJC.
8 Diverse acid selection Require 150 acids to cover chemical space (from 4500 available) Want to bias clustering to consider environment of acid weighted by proximity to acid functional group Single-linkage clustering with Multiple Tanimoto cutoffs e.g. benzoic phenoxyacetic acids in different clusters Use atom pairs, sequences circular fingerprint bits SAR exploration from singleton initial hit Cluster representative = cpd with most neighbours Start with clustering at Tanimoto > 0.7 If cluster too big, iteratively move up a Tanimoto level If cluster too small, iteratively move down a Tanimoto level
9 Acid selection workflow Calculate properties of acids (Mwt, clogp) Representatives ranked by size of cluster they represent Select clusters by quality of rep Pick alternative rep as lowest Mwt cpd Boring bit: wade through singletons
10 Acid selection: learnings ovel clustering method, more intuitive for a chemist! Would have been more intuitive if defined cluster rep as lowest Mwt Simplest, unadorned structure More efficient to identify clusters of interest Then select compounds ot easy to avoid singletons About 1 in 8 (600 / 4800) for this dataset Will get more efficient as IJC skills improve More use of working lists structure matrices Happy with output Human input obviates post processing filtering to remove uglies
11 Post-processing virtual screening Docked lig database with multiple scoring functions Obtained many thouss of putative virtual hits Reduce to 1000 for screening Cluster all hits Calculate all interactions between lig protein Remove examples with anti-h-bonds For each scoring function, pick best cpd from each cluster Subsequent results will define best scoring function
12 Virtual Screening Analysis Form views give a simple intuitive way of visualising each compound/cluster vs physicochemical properties Easy to customise form views tailored to each individual dataset
13 Virtual Screening Analysis Scoring methodology Rank based on cluster number Rank based on docking Yes/o Prioritisation based on structure
14 PDB lig database Extract ligs Cav vol Ensure lig/protein criteria met # lig heavy atoms Generate cavity Generate validate SMILES Canonicalise protein name HYDROGE BOD: parameters : GLY 48 H HYDROGE BOD: parameters : ASP 29 OD1 HYDROGE BOD: parameters : ASP 29 H HYDROGE BOD: parameters : GLY 48 O HYDROGE BOD: parameters : ASP 25 OD1 HYDROGE BOD: parameters : ASP 229 H HYDROGE BOD: parameters : GLY 248 O HYDROGE BOD: parameters : GLY 248 H HYDROGE BOD: parameters : GLY 248 O HYDROGE BOD: parameters : ASP 230 OD1 HYDROGE BOD: parameters : ILE 247 HG2 HYDROGE BOD: parameters : GLY 48 O HYDROGE BOD: parameters : GLY 27 O HYDROGE BOD: parameters : GLY 27 O HYDROGE BOD: parameters : ASP 225 OD1 HYDROGE BOD: parameters : GLY 248 O HYDROGE BOD: parameters : ASP 229 OD1 EDGE-TO-FACE: parameters : ASP 229 EDGE-TO-FACE: parameters : ASP 229 DOOR-PI: parameters : ASP 29 CG DOOR-PI: parameters : GLY 27 C DOOR-PI: parameters : GLY 27 C DOOR-PI: parameters : GLY 248 C DOOR-PI: parameters : ASP 230 OD
15 Uses of PDB lig database Identification of all ligs hitting target or family Substructure search to identify conformational preferences in binding sites interaction preferences starting points for bioisostere generation
16 Isosteres from PDB mining OH IJC substructure search Overlay all CDK2 similar sites H O H O O H O OO O O O O H O O O S O Identify isosteres as groups occupying same space as PhOH
17 Getting more from IJC Make more use of functionality Groovy script for jumping to next cluster in forms Equivalent to trellis from Spotfire Or request increased functionality Visualisations with pie charts Radio slider to simplify queries
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19 The Sexis Team Karl Gibson PhD FRSC, Medicinal Chemist Experienced leader of medicinal chemistry multi-disciplinary project teams delivering cidates to the clinic. Scientific expertise in target validation approaches, HTS triage, hit finding delivering optimal programs to find clinical cidates rapidly. He has experience of ion channels, GPCRs, kinases, enzymes, PPIs nuclear receptors across Pain, Anti-infectives, Genitourinary CS disease areas. Author of over 10 papers book chapters; named inventor on 20 patent applications. Gavin Whitlock PhD FRSC, Medicinal Chemist Areas of expertise include designing molecules for oral, CS, topical or inhaled routes of administration across multiple gene families (enzymes, GPCRs, transporters, kinases) disease areas, including Antiinfectives, Anti-parasitics, Genitourinary, Respiratory Tissue Repair. Author of over 25 papers book chapters; named inventor on 14 patent applications. James Mills PhD FRSC, Computational Chemist Areas of expertise include writing applying novel algorithms to support all stages of the drug discovery process; for example target analysis, HTS triage, lead cidate molecule design, SAR analysis visualisation, virtual screening, analysis of molecular interactions, lig superposition structure-based drug design. Author of over 15 papers; named inventor on 3 patent applications.
20 Medicinal Chemistry We can work with our clients to: Assess prioritise screening hits to generate quality hit-to-lead programs Optimise lead series using an holistic approach to deliver preclinical cidates that meet required biological, ADME, Pharm Sci drug-safety criteria Experience across multiple gene families disease areas: Efficient decision making for series progression or termination Enzymes, GPCRs, nuclear receptors, ion channels, transporters protein-protein interactions Allergy & Respiratory, Anti-infectives, CS, Gastrointestinal, Genitourinary, Obesity, Oncology, Pain, Regenerative Medicine Experience of projects with a wide variety of requirements e.g.: Designing molecules to cross the blood-brain barrier or to be peripherally restricted whilst retaining drug-like properties Identifying designing kinase inhibitors with slow binding kinetics Designing molecules that can be delivered by inhaled or topical routes
21 Computational Chemistry In-house proprietary algorithms to carry out: Molecular superposition, Structure-Based Drug Design SAR analysis visualisation, cutting edge HTS triage Pharmacophore generation searching Bioisosteres based on ChEMBL PDB-derived evidence Calculation manipulation of 2D 3D fingerprints for similarity searching, clustering activity models Transformed ChEMBL PDB databases for efficient searching Commercial software: CCDC suite - GOLD, CSD, Relibase, Isostar ChemAxon - IJC, MarvinBeans Freeware - Openbabel, MySQL, Rasmol Experience with: OpenEye, Daylight, BCI, Pipeline Pilot, Spotfire, Schrodinger, Tripos, Accelrys
22 Training Packages The Sexis Team can design deliver training solutions across a broad range of drug discovery topics, for example: Virtual screening similarity searching Bioisosteres in compound design Molecular interactions, kinetics thermodynamics HTS triage Fragment-based compound design Efficient library design Structure-based drug design Working beyond the rule of 5 ADME for medicinal chemists Safety attrition the role of medicinal chemistry Inhalation by design medicinal chemistry strategies Patents medicinal chemistry
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