The five modules of the BioPharmics Surflex Platform (Tools, Similarity, Docking, xGen, and Affinity) are fully integrated. The full software bundle provides a comprehensive predictive modeling workflow:
- 2D to 3D molecular conversion, with accurate chirality interpretation and enumeration facilities
- Conformer elaboration including complex macrocycles, also supporting the use of NMR restraints
- Protein structure preparation and alignment
- Docking for pose prediction or virtual screening
- Real-space modeling of ligands as conformational ensembles within X-ray density maps
Software is available for Windows, Linux, and Mac platforms, with easy deployment across on-premises workstations and laptops as well as cloud-based computing resources.
September 22, 2023
BioPharmics is growing! We are delighted to have recently been by Optibrium and we are now looking to for the a Business Development Manager to to join us at Optibrium.
The role will focus on sales of the BioPharmics platform for 3D ligand- and structure-based design.
The details on how to apply can be found here.
Version 5.173 Surflex Platform Released
August 22, 2023
This is a minor release (full example set): Release Notes. It includes minor bug fixes and some improvements. See the Downloads page for details.
JMC: Strain/xGen Papers
January 27, 2023
We have published a comprehensive study of bound ligand strain with colleagues from Merck and BMS. This adds to a paper looking at peptide macrocycle strain energetics in the context of real-space refined ligand conformational ensembles. Both studies build on the xGen methodology where we showed that conformational ensembles, without atom-specific B-factors, are better models for ligands in terms of both fit to X-ray density and strain energy.
JCIM: QuanSA and FEP+
December 10, 2021
A focused machine-learning approach to inducing binding site models with QuanSA is shown to be complementary and synergistic with predictions from FEP+ across 17 targets. The paper reports significant reductions in prediction errors and consistent improvements in compound rank-ordering by combining the results of QuanSA and FEP+.