Finding balance in drug discovery through multi-parameter optimisation
Successful drugs require a delicate balance of many properties, such as potency, ADME and toxicity, to meet a project’s therapeutic objective. To make decisions about compound progression and assay selection, the available data must be assessed against project-specific criteria. However, the data on which we base our decisions often come from different sources and can vary in quality, so how can we use this information to make confident decisions? In addition, how can we be sure that the criteria we’re using are the most appropriate?
An augmented approach to generative chemistry
Generative molecular design provides new exciting avenues of chemical space exploration. But how can we use these methods effectively to assess many optimisation strategies and find the compounds destined for success in our projects?
Join Dr Matt Segall and Dr Michael Parker as they explore state-of-the-art generative chemistry, and discuss the importance of an augmented intelligence approach for successful discovery.
From UK-2A to florylpicoxamid: active learning to identify a mimic of a macrocyclic natural product
Scaffold replacement as part of an optimisation process that requires maintenance of potency, desirable biodistribution, metabolic stability, and considerations of synthesis at very large scale is a complex challenge.
Predicting routes of Phase I and II metabolism based on quantum mechanics and machine learning
This peer-reviewed paper in Xenobiotica describes a new method to determine the most likely experimentally-observed routes of metabolism and metabolites based on our WhichP450™, regioselectivity and new WhichEnzyme™ model.
Complex peptide macrocycle optimisation: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design
Systematic optimisation of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead optimisation using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate.
Predicting regioselectivity of cytosolic SULT metabolism for drugs
This paper describes a model to predict whether a particular site on a molecule will be metabolised by cytosolic sulfotransferase enzymes (SULTs).
Predicting reactivity to drug metabolism: beyond CYPs
Introduction Predicting sites of metabolism (SoM) enable chemists to be more efficient in optimising the structure of new chemical entities…
Predicting regioselectivity of AO, CYP, FMO and UGT metabolism using quantum mechanical simulations and machine learning
This paper describes the prediction of the regioselectivity of metabolism by AOs, FMOs and UGTs for humans and CYPs for three preclinical species.
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.
Deep imputation on large-scale drug discovery data
OA paper outlining the practical applications of deep imputation on large-scale drug discovery data. It compares deep learning to traditional QSAR methods.
Multi-parameter optimisation in practice
This webinar describes example applications of multi-parameter optimisation to find high-quality lead compounds.
Predicting reactivity to drug metabolism: beyond P450s – modelling FMOs and UGTs
Methods for modelling two enzyme families, flavin-containing monoxygenases (FMOs) and uridine 5′-diphospho-glucuronosyltransferases (UGTs), to predict reactivity to drug metabolism.
Practical applications of deep learning to impute heterogeneous drug discovery data
This article outlines practical applications of deep learning on drug discovery data. It introduces some of the research behind our…
N- and S-oxidation model of the flavin-containing monooxygenases
Introduction Existing computational models of drug metabolism are heavily focused on predicting oxidation by cytochrome P450 (CYP) enzymes, because of…
WhichP450: a multi-class categorical model
This paper describes the underlying methods and validation of the WhichP450 model, which predicts the most likely Cytochrome P450 isoforms…
ForceGen 3D structure and conformer generation: from small lead-like molecules to macrocyclic drugs
We introduce the ForceGen method for 3D structure generation and conformer elaboration of drug-like small molecules.
Practical applications of matched series analysis
This paper, co-authored with our colleagues at NextMove Software, explores applications of Matched Series Analysis within StarDrop’s Nova module to…
Avoiding missed opportunities by analysing the sensitivity of our decisions
This peer-reviewed article, published in the Journal of Medicinal Chemistry, describes how identifying sensitive criteria can highlight new avenues for exploration, and assist us in avoiding missed opportunities
