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Free Download BioPharmics Surflex Platform 5.191 MultiOS | 2.8 GB

The Surflex Platform consists of the five modules described below. The Surflex Manual contains details of all computational procedures and options within each command-line module. We support Linux (most common variants), Windows, and MacOS. All of the modules are multi-core capable, and very substantial speed-ups are observed with modern multi-core laptops, workstations, and HPC clusters.

Tools Module

Fast and Accurate Small Molecule Processing

The Tools module addresses the most common aspects of small-molecule preparation:

2D to 3D conversion (from SMILES or SDF)

Chirality detection and enumeration

Protonation

Conformer generation

Features and benefits:

Template-free and non-stochastic

Relies on MMFF94sf forcefield for structure derivation

Fast and accurate on typical drug-like ligands, with better coverage of diverse conformations

Fastest and most accurate method for macrocyclic ligands

Capable of incorporating NMR restraints, which is particularly useful for large peptidic macrocycles

Similarity Module

State-of-the-Art 3D Molecular Similarity

The Similarity module implements ligand similarity operations using the eSim method:

Virtual screening

Pose prediction

Multiple ligand alignment

The core eSim methodology is also integrated into the Docking and QuanSA modules.

Features and benefits:

Virtual screening enrichment is both practically and statistically significantly better than alternative methods

Virtual screening speeds of over 20 million compounds per day on a single computing core

Databases of billions of molecules can be screened in hours using cloud-based computing resources

Pose prediction accuracy is substantially better than alternative approaches

Docking and xGen Modules

Top-Tier Solution for Virtual Screening and pose Prediction + Real-Space X-ray Density Modeling of Ligands

The Docking module addresses all aspects of ensemble docking:

Large-scale PDB retrieval and processing

Surface-based binding site alignment using the PSIM method

Fully automatic pocket variant selection to cover the relevant protein conformational variation

Virtual screening

Pose prediction

Feature and benefits:

Automated alignment and selection of appropriate binding site variants

Robust and fully automatic modes for virtual screening and pose prediction\Very extensive validation

Highly accurate non-cognate ligand docking

Directly applicable to synthetic macrocycles, with accuracy equivalent to non-macrocycles

The xGen module implements a novel method for real-space refinement and de novo fitting of ligand ensembles into X-ray density maps:

Models ligand density using conformational ensembles

Avoids atom-specific B-factors as X-ray model parameters

Produces chemically sensible conformers with low strain energy; applicable to complex macrocycles

Yields superior fit to X-ray density than standard fitting approaches

Accessible to non-crystallographers and as part of crystallographic workflows

Affinity Module

Unique Machine-Learning Approach for Prediction Binding Affinity and Pose

The Affinity Module implements the QuanSA (Quantitative Surface-field Analysis) method, which builds physically meaningful models that approximate the causal basis of protein ligand interactions. The module implements integrated procedures for quantitative prediction of both binding affinity and ligand pose, with or without protein structural information:

Multiple ligand alignment for molecular series that include multiple scaffolds

Incorporation of known binding site information

Machine-learning approach to physical binding site model induction using a multiple-instance approach

Prediction of both binding affinity and binding mode of new ligands

Iterative refinement of models with new data

Features and benefits:

Fully automatic model building, including all aspects of ligand conformation and alignment

The binding site model (a "pocket-field") is analogous to a protein binding site, including aspects of flexibility

The pocket-field identifies which pose a new molecule must adopt, and ligand strain is directly modeled

Measurements of prediction confidence and molecular novelty guide user interpretation

Very detailed aspects of molecular surface shape, directional hydrogen bonding preferences, and Coulombic electrostatics are learned

Requires as few as 20 molecules for model induction and is capable of modeling series of hundreds of molecules

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