Publications & Presentations
In this section we post selections of work that the Optibrium team and others have presented or published.
We don’t have an automatic way for you to upload your own articles to this section but if you have any publications or presentations you think might be of interest to other users (it doesn’t have to be about Optibrium’s products) then please get in touch and we’ll help get it posted here for you.
Application of the Alchemite deep-learning methodology to categorical modelling of PK endpoints – ACS National Meeting, 2023
Presentation slides on the Application of the Alchemite deep-learning methodology to categorical modelling of PK endpoints.Charlotte ... Read More
Reaction Based Enumeration: Lessons learned in designing a workflow that chemists want to use – ACS National Meeting, 2023
Presentation slides on Reaction based enumeration: Lessons learned in designing a workflow that chemists want to use.Tamsin Mansley ... Read More
A Distributional Model of Bound Ligand Conformational Strain: From Small Molecules up to Large Peptidic Macrocycles
In this J. Med. Chem. article, written in collaboration with Merck, our xGen method is validated on a set of ~3000 protein-ligand complexes. With this distributional model, strain estimates are consistently lower and better correlated with ligand efficiency than when using classically-modelled ligand coordinates. Read More
Prediction of In Vivo Pharmacokinetic Parameters and Time–Exposure Curves in Rats Using Machine Learning from the Chemical Structure
This article is a collaboration with Intellegens, the University of Cambridge and AstraZeneca. It provides a proof-of-concept study in which Cerella™ is used to predict rat in vivo pharmacokinetic (PK) parameters and concentration–time PK profiles. Read More
Imputation of sensory properties using deep learning – ACS Spring 22
Presentation slides on the imputation of sensory properties. Dmitriy Chekmarev (IFF) and Samar Mahmoud (Optibrium) gave this talk on ... Read More
Synergy and Complementarity between Focused Machine Learning and Physics-Based Simulation in Affinity Prediction
This article, in collaboration with Stephen Johnson at BMS, demonstrates how the QuanSA method is complementary to FEP+, demonstrating improvements in ranking and pKi when a combined approach is used for affinity prediction. Read More
Experimental Validation of Predictive Models in a Series of Novel Antimalarials
This article outlines an open drug discovery competition. Optibrium, in collaboration with Intellegens, developed potential anti-malarials by combining our Cerella™ technology and StarDrop™ drug discovery software. We achieved second place with a compound flagged by Cerella. An unusual compound structure, our entry would not have been considered by human scientists alone. Read More
Imputation of Sensory Properties Using Deep Learning
In this article, the team applies Cerella™'s deep learning imputation methods to physicochemical and sensory data. They compare the results with those from traditional QSAR models, demonstrating significantly improved accuracy using Cerella's methods. Read More
Deep Imputation on Large-Scale Drug Discovery Data
This article outlines the deep learning imputation methods underpinning our Cerella platform, applied to large data sets. It demonstrates significant improvements over commonplace quantitative structure-activity relationship (QSAR) machine learning models Read More
Conformational Strain of Macrocyclic Peptides in Ligand–Receptor Complexes Based on Advanced Refinement of Bound-State Conformers
In collaboration with Merck, we show how the xGen method can be used to refine bound-state conformations of macrocyclic peptide cocrystal structures by building conformational ensembles that are low-energy and fit experimental electron density, providing better guidance for ligand design. Read More
XGen: Real-Space Fitting of Complex Ligand Conformational Ensembles to X‐ray Electron Density Maps
This J. Med. Chem. article is the first report of our xGen methodology, developed in collaboration with Merck. It enables X-ray density ligand fitting and refinement that is suitable for a wide variety of small-molecule ligands, including macrocycles. Read More
Practical Applications of Deep Learning to Impute Heterogeneous Drug Discovery Data
This paper describes Alchemite™, the cutting-edge deep learning method underpinning our Cerella platform, and its success in making predictions even faced with sparse, noisy data. Read More
Structure-Based and Ligand-Based Virtual Screening on DUD-E+: Performance Dependence on Approximations to the Binding-Pocket
Using the DUD-E+ benchmark, we explore the impact of using a single protein pocket or ligand for virtual screening compared with using ensembles of alternative pockets, ligands, and sets thereof. A hybrid approach combining ensemble docking with eSim-based screening produced the best and most consistent performance. Read More
Predicting pKa Using a Combination of Quantum Mechanical and Machine Learning Methods
This study aimed to create a model to predict the propensity of a molecule to lose or gain a proton in water, using a semi-empirical quantum mechanics (QM) approach combined with machine learning (ML). Read More
Electrostatic-field and surface-shape similarity for virtual screening and pose prediction
In collaboration with BMS, we introduce the eSim method for rapid computation of 3D molecular similarity. It combines electrostatic field comparison with comparison of molecular surface-shape and directional hydrogen-bonding preferences. Read More
Practical Applications of Deep Learning to Imputation of Drug Discovery Data
Presented by Ben Irwin, on 28 August 2019 at the ACS National Meeting and Exposition in San Diego, USA Presentation Overview Problems ... Read More
Complex macrocycle exploration: Parallel, heuristic, and constraint-based conformer generation using ForceGen
This study, in collaboration with Merck, demonstrates the application of ForceGen, particularly to a large benchmarking set of macrocycles, generating fast, accurate conformers. Read More
Imputation of Assay Bioactivity Data using Deep Learning
In this study, the team applied a deep learning neural network (the foundations of our Cerella platform) to impute assay pIC50 values. It was shown to outperform other approaches such as traditional QSAR models, and provide predictions with excellent accuracy. Read More
Bigfoot, the Loch Ness Monster, and Halogen Bonds
At the 2018 Streamlining Drug Discovery Symposium in San Diego, David Lawson treated us to this illuminating presentation entitled ... Read More
A Novel Scoring Profile for the Design of Antibacterials Active Against Gram-Negative Bacteria
At the 2nd SCI/RSC Symposium on Antimicrobial Drug Discovery, 12-13 November 2018, Bailey Montefiore, Optibrium - Franca Klingler, ... Read More
Imputation of Protein Activity Data Using Deep Learning
At the US Symposia, Streamlining Drug Discovery 2018 in Cambridge MA, Matthew Segall from Optibrium and Tom Whitehead from Intellegens ... Read More
WaterSwap to Assess Target Druggability
At the 2018 Streamlining Drug Discovery Symposium in San Diego and San Francisco, Adam Kallel gave an insightful presentation on ... Read More
Using AI to Improve the Safety of New Drug Candidates
On 18 October 2018, at the Streamlining Drug Discovery Symposium in Cambridge MA, Nigel Greene gave this fascinating presentation on ... Read More
Hydrogen Bonding: Ab Initio Accuracy From Fast Interatomic Gaussian Approximation Potentials
Mario Öeren gave this presentation at the ACS Fall 2018 National Meeting & Exposition held in Boston, USA. Abstract Non-covalent, ... Read More
Quantitative Surface Field Analysis: Learning Causal Models to Predict Ligand Binding Affinity and Pose
This article introduces our QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. Read More
Library Design for Collaborative Drug Discovery: Expanding Druggable Chemogenomic Space
Dr Kazuyoshi Ikeda, Keio University, gave this presentation at the "Streamlining Drug Discovery" symposium held in Tokyo, Japan on 5 ... Read More
Robotic Drug Discovery: An Automated Design and Synthesis System to Boost SAR Investigations
Dr Tsukasa Ishihara, National Institute of Advanced Industrial Science and Technology (AIST), gave this presentation at the ... Read More
Three Key Factors for Success in Molecular Design: Fast, Visual, Easy
Dr Marcus Gastreich, BioSolveIT, gave this presentation at the "Streamlining Drug Discovery" symposia held in Shanghai, China on 31 ... Read More
Capturing and Applying Knowledge to Guide Compound Optimisation Presentation
Ed Champness, gave this presentation at the "Streamlining Drug Discovery" symposia held in Shanghai, China on 31 May 2018 and Tokyo, ... Read More
Genetic Toxicology: Progress on International Test Guidelines and New Methods
Dr Yan Chang, National Shanghai Center for New Drug Safety Evaluation and Research, gave this presentation at the "Streamlining Drug ... Read More
Theoretical Studies of G-Protein-Coupled Receptors
Dr Xianqiang Sun, Guangzhou Medical University/ Wuxi Apptec, gave this presentation at the "Streamlining Drug Discovery" symposium ... Read More
In Silico Approaches in Genetic Toxicology: Progress and Future
Dr Masamitsu Honma, National Institute of Health Science, gave this presentation at the "Streamlining Drug Discovery" symposium held ... Read More
A Practical View of Structure Activity Relationship (SAR) Analysis in Novartis Shanghai
Dr Zhengtian Yu and Dr Sean Xiao, Novartis, gave this presentation at the "Streamlining Drug Discovery" symposium held in Shanghai, ... Read More
Poster: Intuitive Workflow to Enumerate and Explore Large Virtual Libraries
This poster by Matthew D Segall, Aishling Cooke, James Chisholm, Edmund Champness, Peter Hunt and Tamsin Mansley was presented at the ... Read More
Translating Methods from Pharma to Fragrances and Flavours
Tamsin Mansley gave this presentation at the ACS National Spring Meeting 2018 in New Orleans. Abstract The pharma sector has generated ... Read More
Deep Learning and Chemistry
At our 2018 Drug Discovery Consultants' Day, Professor Bobby Glen of the University of Cambridge gave an excellent overview of ... Read More
Imputation of Protein Activity Data using Deep Learning Presentation 2
At Optibrium's 2018 Drug Discovery Consultants' Day, Dr Gareth Conduit from University of Cambridge and Intellegens Ltd. described ... Read More
Pistoia Alliance AI/Deep Learning Projects and Community
At our Drug Discovery Consultants' Day in March 2018, Nick Lynch gave an overview of the Pistoia Alliances' projects and community on ... Read More
Poster: Closing the loop between synthesis and design: Balancing optimisation of potency with selectivity
This poster by Peter Hunt, Tamsin Mansley, Edmund Champness, Nicholas Foster & Matthew Segall was presented at the ACS Fall 2017 ... Read More
Integrated Cheminformatics to Guide Drug Discovery
Ed Champness gave this presentation at the ACS Fall 2017 National Meeting & Exposition held in Washington DC, USA. Abstract A ... Read More
Poster: Supporting Compound Optimisation in Not-for-Profit and Academic Research
This poster by Matthew Segall1, Tamsin Mansley1, Peter Hunt1, Kelly Chibale2, Tanya Paquet2, James Duffy3 was presented at the RSC ... Read More
Continuing the public benefit of the Carcinogenic Potency Database (CPDB)
Dr Nik Marchetti, Lhasa Ltd, gave this presentation at "Streamlining Drug Discovery" symposium held in Cambridge UK on 18 May ... Read More
Docking – old hat or hats off
Dr Christian Lemmen, BioSolveIT, gave this presentation at the "Streamlining Drug Discovery" symposiums held in San Diego, CA, USA on ... Read More
Resolving the question of on- or off-target toxicity – a case study
Dr Joachim Rudolph, Genentech, gave this presentation at "Streamlining Drug Discovery" symposium held in San Diego, AA, USA on 21 ... Read More
Lead Identification: Where Science Meets Art
Dr Mehran Jalaie, Pfizer, gave this presentation at "Streamlining Drug Discovery" symposium held in San Diego, CA, USA on 21 April ... Read More
Data visualization: Saying it all in a bite-sized chunk
Ed Champness gave this presentation at the ACS Spring 2017 National Meeting & Exposition held in San Diego, USA. Abstract We often ... Read More
Confidently Targeting High Quality Hits from High-Throughput Screening
Matt Segall gave this presentation at the ACS Spring 2017 National Meeting & Exposition held in San Diego, USA. Abstract When ... Read More
Poster: 3D Modelling for the Masses: A Universal Interface for Easy Access to Expertly Prepared 3D Models
This poster by Fayzan Ahmed, Tamsin Mansley, Chris Leeding, Edmund Champness, Peter Hunt & Matthew Segall was presented at the ACS ... Read More
Combining quantum and QSAR methods for prediction of acid dissociation constants
Layla Hosseini-Gerami1,2, Rasmus Leth1, Peter Hunt1, Matthew Segall1. 1 Optibrium Limited, Cambridge, UK2 University of Leeds, Leeds, ... Read More
ForceGen 3D structure and conformer generation: From small lead-like molecules to macrocyclic drugs
Here, we introduce the ForceGen method for 3D structure generation and conformational elaboration, which does not rely on distance geometry, precalculated molecular templates, or stochastic sampling. Read More
Poster: BSEP, MRP, AND DILI… Just a bad hand at Scrabble?
This poster was presented by Nick Foster at the 11th International ISSX Meeting, Busan, Korea in June 2016 Abstract Cholestatic ... Read More
Practical Applications of Matched Series Analysis
This paper explores applications of Matched Series Analysis within StarDrop's Nova module to SAR transfer, binding mode suggestion, and data point validation. Read More
Avoiding Missed Opportunities by Analysing the Sensitivity of our Decisions
This peer-reviewed article describes how identifying sensitive criteria can highlight new avenues for exploration, and assist us in avoiding missed opportunities. Read More
Medicinal Chemistry is an art, when you don’t understand the data
Dr Jeremy Edmunds, Abbvie, gave this presentation at "Streamlining Drug Discovery and Development" held in Cambridge, MA, USA on 11 ... Read More
TB Alliance Drug Discovery and Development: Harnessing Global Resources to Address a Global Disease
Dr Chris Cooper, TB Alliance, gave this presentation at "Streamlining Drug Discovery and Development" held in San Francisco, CA, USA ... Read More
Structure Guided Design and Optimization of Selective Kinase Inhibitors from Fragment Starting Points
Dr Steve Woodhead, Takeda, gave this presentation at "Streamlining Drug Discovery and Development" held in San Francisco, CA, USA on ... Read More
Cheminformatics from the end-user perspective: Past, present and future
Dr Paul Greenspan, Takeda, gave this presentation at "Streamlining Drug Discovery and Development" held in Cambridge, MA, USA on 11 ... Read More
Speeding up and improving the Identification of a potent B2 agonist as a growth promoter for cattle
Dr Ashley Fenwick, Zoetis, gave this presentation at "Streamlining Drug Discovery and Development" held in Cambridge, MA, USA on 11 ... Read More
User-friendly Database Querying for Decision-Making in Drug Discovery
This poster was presented by Chris Leeding, Ed Champness, Chris Mills*, Andrew Lemon*, Ashley Fenwick$ and Matt Segall at BioIT World ... Read More
Bridging the dimensions: Seamless integration of 3D structure-based design and 2D structure-activity relationships to guide medicinal chemistry
Matt Segall gave this presentation at the ACS Spring National Meeting & Exposition held in San Diego, USA on 13 March ... Read More
Gaussian processes: We demand rigorously defined areas of uncertainty and doubt
Ed Champness gave this presentation at the ACS Spring National Meeting & Exposition held in San Diego, USA on 16th April ... Read More
Closing the Loop Between Synthesis and Design
Tamsin Mansley gave this presentation at the ACS Spring National Meeting & Exposition held in San Diego, USA on 13th March ... Read More
Extrapolative prediction using physically-based QSAR
This study demonstrates the application of the Surflex-QMOD method for 3D-QSAR on a large, diverse public data set, showcasing recent algorithmic and workflow enhancements to improve the generated models. Read More
When Two are not Enough: Lead optimisation beyond matched pairs
This article, co-authored with Noel O'Boyle and Roger Sayle of Optibrium NextMove Software, was published in Drug Discovery World. It ... Read More
Data visualization: New directions or just familiar routes?
Ed Champness gave this presentation at the ACS Fall 2015 National Meeting & Exposition held in Boston, USA on 19th August ... Read More
Modeling ABC transporters as potential DILI targets
Matt Segall gave this presentation at the ACS Fall 2015 National Meeting & Exposition held in Boston, USA on 16th August ... Read More
Knowledge-Guided Docking: Accurate Prospective Prediction of Bound Configurations of Novel Ligands using Surflex-Dock
This study presents a new benchmarking data set for assessing pose prediction using molecular docking called PINC. It also presents new approaches within Surflex Dock that use structural information known prior to a particular cutoff-date to make predictions on ligands whose bounds structures were determined later, showing excellent results. Read More
Webinar: Beyond Matched Pairs
Read the presentation "Beyond Matched Pairs: Applying Matsy to predict new optimisation strategies" at the joint NextMove ... Read More
The Challenges of Making Decisions Using Uncertain Data
This paper discusses the challenges of using uncertain experimental data to make confident decisions on the selection of compounds. Read More
Analyzing Selectivity Through Multi-dimensional Activity Cliff Analysis
Dr Tim Cheeseright, Cresset, gave this presentation at the "Guiding Optimal Compound Design and Development Symposium" held in ... Read More
Understanding Compound Quality
Dr Paul Leeson, Paul Leeson Consulting Ltd, gave this presentation at the "Guiding Optimal Compound Design and Development Symposium" ... Read More
Predicting Adverse Drug Reactions: What Works and What Doesn’t
Dr Nigel Green, Pfizer, gave this presentation at the "Guiding Optimal Compound Design and Development Symposium" held in Cambridge, ... Read More
Modeling of Chemical and Physical Stability of Pharmaceuticals
Dr Yuriy Abramov, Pfizer, gave this presentation at the "Guiding Optimal Compound Design and Development Symposium" held in Cambridge, ... Read More
Development of a Drug Discovery Simulation Laboratory Exercise in the Pharmaceutical Sciences Graduate Program Curriculum
Dr Chase Smith, Massachusetts College of Pharmacy and Health Sciences, gave this presentation at the "Guiding Optimal Compound Design ... Read More
Improving the Plausibility of Success in Drug Discovery with the Use of Inefficient Metrics
Dr Mike Shultz,Novartis, gave this presentation at the "Guiding Optimal Compound Design and Development Symposium" held in Cambridge, ... Read More
Practical Application of Multi-Parameter Optimization to Guide Successful Drug Discovery
Dr Matt Segall, Optibrium, gave this presentation at the "Guiding Optimal Compound Design and Development Symposium" held in ... Read More
Development of a Structure Generator to Explore a Target Area on Chemical Spaces
Dr Kimita Funatsu gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan ... Read More
Knowledge-based Small Molecule and Antibody Design Strategies
Mark Swindells gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan on ... Read More
Breaking Free from Chemical Spreadsheets
This article explores the benefits of a more intuitive and flexible approach to viewing and interacting with drug discovery data, the ... Read More
Beyond Matched Pairs
Noel O'Boyle of NextMove Software gave this presentation "Beyond Matched Pairs: Using matched series for activity prediction" at our ... Read More
Structure-based drug discovery in Shanghai Hengrui
Dr Qiyue (Jerry) Hu gave this presentation at the International Symposium on Compound Design Technologies held in Shanghai, China on ... Read More
Integrated predictive ADME tools for optimising exposure and safety in drug discovery and development
Dr Jianling Wang gave this presentation at the International Symposium on Compound Design Technologies held in Shanghai, China on 21 ... Read More
Chemical and Protein Structural Basis for Biological Crosstalk Between PPAR-alpha and COX Enzymes
Here we present an analysis of novel drug/target predictions, focusing on those that were not obvious based on known pharmacological crosstalk. We show how protein structural information can be exploited to bolster predictions of polypharmacology from ligand-based computations. Read More
Novel lead optimization strategy of BACE I inhibitors for the treatment of Alzheimer’s disease by QSAR and PBPK modeling
This poster was presented by Jinju Byeon, Professor Young Shin and co-authors from Chungnam National University at the 2014 ... Read More
Improving the Chance of Success Where an Outcome Can’t be Predicted
Matt Segall gave this presentation at the ACS Fall 2014 National Meeting & Exposition held in San Francisco, USA on 10ᵗʰ August ... Read More
Visualising Structured Compound Data in an Unstructured Way
This Poster was presented at the ACS Fall 2014 National Meeting & Exposition held in San Francisco, USA on 12th August ... Read More
Advances in Multi-parameter Optimisation Methods for de Novo Drug Design
This review article discusses recent developments in the methods and opinions around multi-parameter optimisation, focusing on applications to de novo drug design and illustrated with published examples. Read More
Addressing Toxicity Risk when Designing and Selecting Compounds in Early Drug Discovery Presentation
Ed Champness gave this presentation at the ACS Spring 2014 National Meeting & Exposition held in Dallas, USA on 19th March ... Read More
Predictive Application of Bioisostere Transformations to Identify Novel High Quality Compound Ideas
Ed Champness gave this presentation at the ACS Spring 2014 National Meeting & Exposition held in Dallas, USA on 17th March ... Read More
Advances in Multi-Parameter Optimisation: Targeting the ‘best’ profile for your project’s objectives
Ed Champness gave this presentation at the ACS Spring 2014 National Meeting & Exposition held in Dallas, USA on 16th March ... Read More
The Significance of Protein Structure Data Set Choices for in-silico Drug Discovery: Design of BACE1 Inhibitors
Dr Yoshio Hamada gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan ... Read More
Matched Molecular Pair and Activity Cliffs: the Next Dimension
Tim Cheeseright gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan ... Read More
New, Transparent, Statistical Approaches to Toxicity Prediction
Thierry Hanser gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan on ... Read More
Linear Expression by Representative Energy Terms: A Novel QSAR Procedure Using Theoretical Computations on Protein-Ligand Complexes
Dr Hiroshi Chuman gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan ... Read More
ChEMBL an Open Data Resource of Medicinal Chemistry and Patent Data
John Overington gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan ... Read More
Finding and Applying Multi-Parameter Rules to Guide Successful Drug Discovery
Matt Segall gave this presentation at the International Symposium on Compound Design Technologies held in Tokyo and Osaka, Japan on 19 ... Read More
Finding the Rules for Successful Drug Optimisation
In this drug optimisation article, coauthored with Pfizer we discuss new 'rule induction' methods. Read More
A structure-guided approach for protein pocket modeling and affinity prediction
We present a hybrid structure-guided strategy that combines molecular similarity, docking, and multiple-instance learning such that information from protein structures can be used to inform models of structure–activity relationships. Read More
Addressing Toxicity Risk when Designing and Selecting Compounds in Early Drug Discovery
In this article, we discuss the application of expert knowledge-based predictions of toxicity. We highlight how they can be used with MPO... Read More
Protein Function Annotation By Local Binding Site Surface Similarity
This study presents some updates to the Surflex-PSIM method, validated on apo/holo cognate protein pairs. The method now supports fully automatic binding site detection and is fast enough to screen comprehensive databases of protein binding sites. Read More
Implementation of multi-criteria decision making (MCDM) tools in early drug discovery processes
Marie Ledecq from UCB Pharma gave this presentation at the ACS National Spring Meeting 2013. Abstract The current trend in medicinal ... Read More
Exploring the chemical space of screening results
Ed Champness gave this presentation at the ACS National Spring Meeting 2013. Abstract When faced with the results from a screening ... Read More
Knowledge based prediciton of toxicity
David Watson gave this presentation at the "Addressing toxicity in drug discovery" workshop during the ACS National Spring Meeting ... Read More
Finding Multi-parameter Rules for Successful Optimization
Matt gave this presentation at the ACS National Spring Meeting 2013 in New Orleans. Abstract Multi-parameter optimization (MPO) is ... Read More
Protein-Protein Interactions and Inhibitors
Alan Naylor led a stimulating discussion on drug discovery targeting inhibition of protein-protein interactions at our 2012 Drug ... Read More
QSAR Modelling of HLM Stability
This talk was presented by Dr Alexey Zakharov at the American Chemical Society 2012 Fall Meeting in Philadelphia. Abstract The most ... Read More
Preprint: Considering the Impact of ‘Drug-like’ Properties on the Chance of Success
In the paper we review the strengths and weaknesses of these different definitions of 'drug-like' properties and measures of 'drug-likeness.' Read More
Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization
This study demonstrates the application of Surflex-QMOD, a computational approach for binding affinity prediction, to an iterative, temporal lead optimisation exercise. Read More
Decision Support in Discovery through StarDrop
Dr Vijay Gombar gave this presentation at the StarDrop User Group Meeting and Workshop during the ACS National Fall Meeting ... Read More
Comprehensive comparison of automatically generated QSAR models of target potency
Matt gave this presentation at the ACS National Spring Meeting 2012. Abstract Automatic QSAR model building methods are now readily ... Read More
Can We Really Do Computer-aided Drug Design?
Matt gave this presentation at the ACS National Spring Meeting 2012. Abstract We will explore the accuracy of current computational ... Read More
Relative Drug Likelihood: Going Beyond Drug-Likeness
Matt gave this presentation at the ACS National Fall Meeting 2012. Abstract Many approaches have been used to characterise compounds ... Read More
Overcoming Psychological Barriers to Good Decision-making in Drug Discovery
Matt gave this presentation at the Keystone Symposium, Addressing the Challenges of Drug Discovery 2011. Abstract Better individual ... Read More
Does Your Model Weigh the Same as a Duck?
This article discusses some of the pitfalls of computer-aided drug design, such as confirmation bias, in the context of off-target predictive modeling, QSAR, molecular similarity computations, and docking. Examples are shown that avoid such problems. Read More
Chemical Structural Novelty: On-Targets and Off-Targets
This study presents s framework for combining multiple similarity computations, along with its systematic application to 358 drugs with overlapping pharmacology. Read More
Applying Med Chem Transformations and Multi-Parameter Optimisation
This article on applying med chem transformations and multi-parameter optimisation describes the concepts and algorithms underlying StarDrop’s Nova module. Read More
Multi-parameter Optimisation in Drug Discovery
Matt Segall, Optibrium's CEO, gave a presentation on Multi-parameter Optimisation in Drug Discovery at ELRIG Drug Discovery.Abstract A ... Read More
Multi-Parameter Optimisation Review Article
In this review, we survey the range of methods used for MPO in drug discovery, compare their strengths and weaknesses and present some example applications. Read More
Poster: Guided Application of Med Chem Rules to Generate Good Ideas
Matt presented this poster at RICT in July 2011. Abstract: Computational tools can guide the selection of high quality compounds, with ... Read More
Guided Application of Med Chem Rules to Generate ‘Good’ Ideas
Matt gave this presentation at the ACS Spring meeting 2011 in Anaheim. Abstract Computational tools can guide the selection of high ... Read More
Making Priors a Priority Presentation
Matt gave this presentation at the ACS Spring meeting 2011 in Anaheim. Abstract When we build a predictive model of a drug property we ... Read More
Making Priors a Priority Publication
This article discusses the importance of ‘priors’ in assessing models in different contexts, covering a critical issue that the community needs to address address in order to use the predictive models that we build to the greatest effect. Read More
Poster: Automatic Generation and Validation of QSAR Toxicity Models
Matt presented this poster at ISSX in October 2011. Abstract: Whether compounds are intended as drugs, cosmetics, agrochemicals or for ... Read More
Issues in the Interpretation, Understanding, and Use of Drug Discovery Data
Dr Terry Stouch gave this presentation as part of the first StarDrop User Group Meeting and Workshop at the ACS Fall meeting 2010 in ... Read More
Exploring Project Spaces to Quickly Identify High Quality Compounds
In this presentation Ed Champness demonstrates some of StarDrop's features for identifying and selecting compounds, using chemical ... Read More
Poster: Maximising compound value by making good decisions:
Matt presented this poster at MipTec in September 2010. Abstract: People are notoriously poor at making good decisions based on ... Read More
Medicinal chemists are people too: And that’s a problem
Dr Mikel Moyer gave this presentation as part of the first StarDrop User Group Meeting and Workshop at the ACS Fall meeting 2010 in ... Read More
In silico ADME/Tox: Why models fail: Why models work
Dr Terry Stouch, Consulting in Drug Discovery and Design Practice, Technologies, Process at Princeton, NJ and Duquesne University gave ... Read More
Overcoming psychological barriers to good discovery decisions
This article explores the psychological barriers and risks of cognitive biases to R&D decision-making. It contrasts current practice with the use of evidence-based medicine Read More
A rational approach to risk reduction
Dr Andrew Chadwick, Consultant (Life Sciences and Healthcare) at Tessella gave this presentation on "Rational Approach to Risk ... Read More
Gaussian Processes for Classification: QSAR Modeling of ADMET and Target Activity
In this study, the researchers look to solve classification quantitative structure−activity relationship (QSAR) modelling problems using Gaussian processes. Read More
Visual analyses for guiding compound selection and design
In this presentation Ed Champness considers the decision-making challenges faced by drug discovery scientists and presents some visual ... Read More
Beyond Profiling: Using ADMET Models to Guide Decisions
Summary The main use of ADMET models, whether in silico or in vitro,tends to be molecule ‘profiling’; identifying compounds which are ... Read More
Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility
In this study, our researchers combined an automatic model generation process for building QSAR models with the Gaussian Processes machine learning method. Read More
ADMET Property Prediction: The State of the Art and Current Challenges
This article explores ADMET property prediction by QSAR methods. It covers statistical modelling techniques, molecular descriptors, data sets applications and challenges faced. Read More
Improving Drug Discovery Efficiency via In Silico Calculation of Properties
Dan Ortwine gave this presentation as part of a workshop at the ISSX North American meeting in Baltimore, MD, USA, 2009. The workshop, ... Read More
Poster: Guiding the Decision-Making Process to Identify High Quality Compounds
Defining a property profile is subjective and often leads to lengthy, interdisciplinary discussions about the criteria and their ... Read More
Poster: Application of in Silico (ADMEnsa Interactive) and ADME/PK Assays in the Identification of New Chemical Entities (NCEs) for Pre-Clinical Evaluation
The current paradigm of drug discovery utilising chemical library synthesis coupled with high throughput screening technologies often ... Read More
Predictive ADME Models in Drug Discovery: Can You Trust Them? Can You Afford Not To?
In this presentation, Alan Beresford discusses drug discovery and considers how ADME models fit into the process as a step necessary ... Read More
Gaussian Processes for Classification
O. Obrezanova and M. D. Segall, White Paper In this article, Olga describes how we extend the application of Gaussian Processes ... Read More
Automated QSAR Modelling to Guide Drug Design
In this presentation Olga Obrezanova describes an automated process for building QSAR models (now available as part of StarDrop as ... Read More
Poster: Opening the ‘Black Box’: Interpreting in Silico Models to Guide Compound Design
In silico predictive models are now widely used to predict a range of molecular properties and help prioritise molecule for synthesis. ... Read More
Poster: Automated QSAR Modeling to Guide Drug Design
The rapid design-test-redesign cycles of modern drug discovery and the demand for fast model (re)building whenever data becomes ... Read More
The journey from Drug Discovery to Drug Design: How far have we travelled?
In this presentation, Matt Segall talks about the differences between "design" and "discovery" and considers two different analogies ... Read More
Xemistry Core Competencies
In this presentation Wolf Ihlenfeldt describes some of Xemistry's core competencies. Xemistry is one of Optibrium's partners, ... Read More
Gaussian Processes: a method for automatic modelling of ADME properties
In this presentation Olga Obrezanova talks about Gaussian Processes - a powerful computational method for QSAR modelling. Olga starts ... Read More
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