Publication for Discovery Projects

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.

Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecules

Predicting metabolism at an early stage is important in maximising the chance of a drug’s success. However, accurate, useful models…

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.

Unmasking the true identity of rapamycin’s minor conformer

The solution structure of the minor conformer of rapamycin was investigated using a combination of NMR techniques and computational methods

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).

A distributional model of bound ligand conformational strain: from small molecules to large peptidic macrocycles

We show that the distribution of expected global strain energy values is dependent on molecular size in a superlinear manner. The distribution of strain energy follows a rectified normal distribution whose mean and variance are related to conformational complexity.

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.

Synergy and complementarity between focused machine learning and physics-based simulation in affinity prediction

We present results on the extent to which physics-based simulation (exemplified by FEP+) and focused machine learning (exemplified by QuanSA) are complementary for ligand affinity prediction.

Experimental validation of predictive models in a series of novel antimalarials

In this study, we identified a new antimalarial with an unusual structure – the only compound in the competition to be proven active, opening up new chemistry for exploration.

Imputation of sensory properties using deep learning

In this article, the team demonstrates the application of Alchemite™, a deep learning imputation method which underpins our Cerella™ technology, to physicochemical and sensory data.

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.

Conformational strain of macrocyclic peptides in ligand–receptor complexes based on advanced refinement of bound-state conformers

To better understand conformational propensities, global strain energies were estimated for 156 protein-macrocyclic peptide cocrystal structures.

Cookies

Filter by