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…
New PyMOL interface extends access to Optibrium’s structure-based design method Surflex-Dock CAMBRIDGE, UK, 04 February 2025 – Optibrium, a leading…
What comprises large molecules? When we talk about “large molecules,” we often think of biologics like monoclonal antibodies, proteins, and…
CAMBRIDGE, UK, 22 October 2024 – Optibrium, a leading developer of software and AI solutions for molecular design today announced…
In this paper, we describe an extended benchmark for non-cognate docking of macrocyclic ligands, and the superior performance of 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.
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.
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.
To better understand conformational propensities, global strain energies were estimated for 156 protein-macrocyclic peptide cocrystal structures.
We report a new method for X-ray density ligand fitting and refinement that is suitable for a wide variety of small-molecule ligands, including macrocycles.
ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks.
Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction.
We present an approach that uses structural information known prior to a particular cutoff-date to make predictions on ligands whose bounds structures were determined later. The knowledge-guided docking protocol was tested on a set of ten protein targets using a total of 949 ligands.
Here we present an analysis of novel drug/target predictions, focusing on those that were not obvious based on known pharmacological crosstalk.
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.