The five modules of the 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.
Version 5.1 Surflex Platform Released
September 17, 2020
Surflex Platform v5.1 is a minor release to include the new xGen module. Version 5.0 was a major update that included improvements in the use of known crystallographic poses to help guide multiple-ligand alignment and in the iterative refinement process for QuanSA model building.
JCIM: Improvements in Docking
April 9, 2020
The latest improvements in Surflex-Dock, especially with respect to virtual screening, has been published in a special issue of JCIM. Ensemble docking is shown to produce much better results than single-structure docking on the DUD-E+ benchmark. Ligand-based methods employing eSim are shown to be competitive as well. Hybrid approaches are much better than single-mode approaches.
JCAMD: The eSim Similarity Method
October 24, 2019
We have published, with Stephen Johnson, a new 3D molecular similarity method (called eSim) that directly incorporates Coulombic field comparison with surface-shape and hydrogen-bonding comparison. It is both faster and more accurate that commonly used alternatives, both for virtual screening and pose prediction. The paper is entitled “Electrostatic-field and surface-shape similarity for virtual screening and pose prediction.”
JMC: xGen Real-Space Ligand Refinement
September 2, 2020
We have published a paper with colleagues from Merck entitled “XGen: Real-Space Fitting of Complex Ligand Conformational Ensembles to X‐ray Electron Density Maps.” We show 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. Ligand refinement is demonstrated on a large set of macrocycles, and de novo fitting is demonstrated on a set of non-macrocyclic small molecules. The xGen approach offers a new paradigm for ligand modeling within X-ray diffraction data..