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…
Leveraging structure-based and ligand-based techniques in drug discovery
In our recent article in Innovations in Pharmaceutical Technology, we explore the principles, techniques, and applications of structure-based and ligand-based…
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,…
Optibrium Widens Access to Industry-Leading Docking Method
New PyMOL interface extends access to Optibrium’s structure-based design method Surflex-Dock CAMBRIDGE, UK, 04 February 2025 – Optibrium, a leading…
How can I model large molecules like macrocycles?
What comprises large molecules? When we talk about “large molecules,” we often think of biologics like monoclonal antibodies, proteins, and…
How do I get budget approval for drug discovery software?
Buying software for your company can be a challenge. Every organisation does things differently, and there is often no handbook…
Optibrium demonstrates superior molecular docking method for small molecules and macrocycles
CAMBRIDGE, UK, 22 October 2024 – Optibrium, a leading developer of software and AI solutions for molecular design today announced…
Physical Parameter Estimation vs. Pure Machine-Learning for Drug Design
Nearly all computational methods in the CADD field depend on parameters whose values are derived from various types of experimental…
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…
Macrocyclic peptide optimisation: Integrating computational approaches with biophysical data
Introduction Using the integrated set of computational methods within the BioPharmics™ Platform, macrocycles can be effectively modelled for lead optimisation.…
Molecular docking: Extrapolating to new scaffolds with Surflex-Dock
Interested in improving your binding mode predictions? Surflex-Dock is a unique method for molecular docking, offering automatic pipelines for ensemble docking, applicable to both small molecules and large peptidic macrocycles alike.
Macrocyclic lead optimisation
Macrocycles are becoming increasingly popular in drug discovery, due to their vast potential against previously “undruggable” targets. But the size…
Challenges and approaches in 3D ligand-based drug design
Ann and Ajay discuss the science behind and applications of the eSim molecular similarity method, a ligand-based drug design approach which considers surface-shape, electrostatics, and directionally sensitive hydrogen-bonding when comparing two molecules.
3D ligand-based drug design: webinar
In this webinar, we demonstrate intuitive workflows for 3D ligand-based drug design
eSim3D: electrostatic-field and surface-shape similarity for ligand-based drug design
In this webinar, we present eSim3D, a novel ligand-based drug design approach based on electrostatic-field and surface-shape similarity coupled with unique conformational search capabilities, offering unprecedented accuracy and performance.
Chemical Structural Novelty: On-Targets and Off-Targets
To compare chemical structures, we can look at a number of 2D and 3D characteristics. In this paper, a group of 358 drugs with overlapping pharmacology were assessed for chemical similarity, using a new framework.
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.”
