Optibrium demonstrates accelerated lead optimisation in complex agrochemical development
Optibrium’s QuanSA 3D-QSAR method uses an active learning approach to successfully and more efficiently identify a mimic of a macrocyclic natural product
Cerella case studies ebook
In this ebook we demonstrate our deployable AI discovery platform, Cerella™. Browse real-world stories of success from our collaborations with AstraZeneca, Genetech, Takeda Pharmaceuticals, Constellation Pharmaceuticals and many more.
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.
Optibrium’s quantum mechanics and machine learning methods predict routes of drug metabolism
Peer-reviewed study published in Xenobiotica describes an innovative new method that predicts the routes and products of Phase I and II metabolism with high sensitivity and greater precision than
other approaches
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.
Optibrium acquires BioPharmics LLC, expanding its 3D drug design and visualisation offering
BioPharmics’ Drs Ajay Jain (CEO) and Ann Cleves (Director of Applied Science) join the Optibrium team as Vice Presidents in the newly-created BioPharmics Division
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.
Optibrium releases 3D ligand-based design module for StarDrop drug discovery software
Innovative predictive methods support virtual screening and compound design in the absence of 3D structure 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.
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…
