Download BioPharmics Surflex Platform 5.191 full license
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Description of BioPharmics Surflex Platform 5.191
It comprises five modules: Tools, Similarity, Docking and xGen, and Affinity. Each module is designed to streamline specific drug discovery and molecular modeling tasks, providing researchers with a robust and efficient workflow.
Tools Module
The Tools module is focused on preparing and processing small molecules. It offers a suite of features that ensures fast and accurate handling of molecular data.
Features and Benefits:
2D to 3D Conversion: Transforms SMILES or SDF input into 3D structures.
Chirality Detection: Identifies and enumerates chiral centers.
Protonation: Adjusts protonation states of molecules.
Conformer Generation: Produces diverse conformations of molecules.
This module uses the MMFF94sf forcefield for structure derivation, ensuring high accuracy. It’s especially adept at processing macrocyclic ligands and can incorporate NMR restraints, which is crucial for large peptidic macrocycles.
Similarity Module
The Similarity module excels in performing 3D molecular similarity operations using the eSim method, making it an invaluable tool for virtual screening and pose prediction.
Features and Benefits:
Virtual Screening: Efficiently screens large molecular databases, with speeds exceeding 20 million compounds per day on a single core.
Pose Prediction: Provides highly accurate pose predictions.
Multiple Ligand Alignment: Aligns multiple ligands for comparative analysis.
The module’s virtual screening capabilities are practically and statistically superior, making it a powerful tool for high-throughput screening.
Docking and xGen Modules
These modules provide a comprehensive solution for virtual screening, pose prediction, and X-ray density modeling of ligands.
Docking Module Features:
PDB Retrieval: Automates large-scale retrieval and processing of PDB structures.
Binding Site Alignment: Uses PSIM for surface-based alignment.
Pocket Variant Selection: Automatically selects relevant protein conformations for virtual screening.
xGen Module Features:
Ligand Density Modeling: Uses conformational ensembles for real-space refinement.
X-ray Density Fitting: Produces chemically sensible conformers with low strain energy, improving fit to X-ray density.
These modules are highly validated and can handle both non-macrocyclic and macrocyclic ligands with high accuracy.
Affinity Module
The Affinity module uses the QuanSA method for predicting binding affinity and ligand pose, integrating machine learning to enhance predictive accuracy.
Features and Benefits:
Binding Affinity Prediction: Quantitatively predicts binding affinity using physical models.
Pose Prediction: Accurately predicts ligand binding modes.
Model Refinement: Iteratively refines models with new data, improving accuracy over time.
This module builds comprehensive models that approximate the causal basis of protein-ligand interactions, making it a unique and powerful tool for drug discovery.
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