Why does conformational flexibility matter in drug design?
What are conformational ensembles? A conformational ensemble is a collection of the different 3D shapes a molecule can adopt in…
How much does 3D molecular modelling software cost?
Introduction 3D molecular modelling plays a vital role in modern drug discovery, offering powerful applications to streamline research, reduce costs,…
Physical model induction with QuanSA™: Affinity prediction that is synergistic with simulation-based methods
The QuanSA method To define a ‘pocket field’, an initial alignment of all training molecules is constructed and function parameters…
How to perform fast and accurate 3D ligand-based affinity predictions
Binding affinity prediction continues to be a challenge in computer-aided drug design, especially in the absence of a high-quality target…
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
Does your model weigh the same as a Duck?
This article discusses logic fallacies in the context of off-target predictive modelling.
Iterative refinement of a binding pocket model: active computational steering of lead optimisation
Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for “synthesis.”
Extrapolative prediction using physically-based QSAR
Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction.
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.
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.
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.
Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose
We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone.
