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3D Computational Modelling for Drug Design

Fast, robust, and accurate 3D modelling from small molecules to large macrocycles


3D Ligand-Based and

Structure-Based Design

With industry-leading ligand- and structure-based design software, you can quickly generate accurate conformational ensembles, even for complex macrocycles, predict bound ligand poses and binding affinities without requiring protein structural information, and access rapid, robust virtual screening.


3D Design Platform

The BioPharmics platform enables you to perform:

3D Chemical Toolkit Operations

Use fast, accurate and template-free conformer generation with ForceGen, even on complex macrocycles; generate 3D structures from 2D, including chirality enumeration and heuristic protonation at physiological pH; produce large-scale conformer databases for virtual screening

Docking and Structure-Based Design

Automate your protein preparation and alignment; access top-tier enrichments and accurate pose prediction with Surflex-Dock; use xGen to model conformational heterogeneity of bound ligands with real-space fitting of ligands into X-ray electron density

Ligand-Based Modelling

Screen millions of compounds and achieve industry-leading enrichment with eSim; predict relative bound poses of structurally diverse ligands using eSim’s multiple-ligand alignment method

Affinity Prediction

Use multiple-instance machine-learning to predict ligand binding affinity and pose with QuanSA’s physically-motivated, accurate, and scaffold-independent models

Using structured-based drug design to dock a protein in the BioPharmics platform

Achieve better pose prediction and screening enrichment by docking to protein ensembles with the BioPharmics platform.

The BioPharmics platform is supported by peer-reviewed studies published in collaboration with leading global pharmaceutical companies.

Method Highlights

Screenshot of structured-based drug design approach using ForceGen

Model macrocycles with ease, using NMR constraints to increase efficiency and accuracy. Above is Aureobasidin A, a macrocyclic antifungal compound, modelled using NMR constraints.



Struggling to generate accurate conformers for your macrocycles or structurally-novel compounds? With ForceGen, you can identify the most likely conformers for all your compounds, from small molecules to large macrocycles.

ForceGen’s patented, template-free method systematically applies 3D physical manipulations to explore the geometrical configurations of your ligands, generating ensembles of low-energy conformers. It can evaluate complex macrocycles in minutes, with the option of applying NMR constraints to increase search efficiency.



Access top-tier docking enrichment and accurate pose prediction with reliable automated docking procedures. Automate protein preparation and reduce inappropriate bias with Surflex-Dock’s easy-to-use workflows. Surflex-Dock supports your 3D structure-based design efforts, allowing you to model ligands from small molecules to large macrocycles.

With Surflex-Dock, ensembles of protein conformations take into account the variability in the binding pocket and relevant interactions, to give a more realistic picture of molecular docking.

Surflex-Dock enables highly accurate non-cognate ligand docking, so you can correctly predict poses for future potential ligands for your protein complex, making use of known prior bound ligand poses with eSim.

Using structured-based drug design with Surflex-Dock

Surflex-Dock’s automated protein preparation allows you to map ensembles of protein conformations for accurate, reliable 3D structure-based drug design. Top, you can see 42 CDK2 protein structures with 5 small ligands docked and below is a molecular similarity comparison of two CDK2 ligands.

Structured-based drug design with eSim

Use observer points to map a scaffold-independent surface representation of your molecule. Here 2-pyrollidone is seen surrounded by observer points (top), and the molecular similarity of a macrocyclic ligand of estrogen receptor alpha is compared to an alignment target (bottom).



Looking for fast, reliable 3D ligand-based drug design and virtual screening? eSim considers molecular similarity from a protein’s perspective of a ligand, creating a surface representation of the molecule based on shape, electrostatic field, and hydrogen-bond preferences. This information can then be used to:

  • Carry out large scale, high performance 3D virtual screening against known actives to find novel potent compounds
  • Achieve significantly higher enrichment in your virtual screens than leading competitors
  • Inform 3D design with industry-leading pose prediction

eSim is also available as part of our StarDrop drug discovery platform, so you can include 3D ligand-based drug design as part of your integrative, intuitive drug discovery workflow.



The BioPharmics QuanSA (Quantitative Surface-field Analysis) method lets you predict the affinity of your ligands using machine learning models based on physical principles. QuanSA’s 3D ligand-based models achieve equivalent accuracy to market-leading methods such as FEP+, while providing transferablility, even across scaffolds. With QuanSA, you can:

  • Predict binding affinity without protein structural information
  • Build accurate affinity models, even with only a small number of known actives
  • Find novel active compounds with prediction confidence metrics
Structured-based design using QuanSa

Modelling Meclonazepam using the QuanSA causal machine learning method to build a pocket-field model.


Try the BioPharmics platform

Interested in seeing the BioPharmics platform in action? Complete the form and a member of our team will get in touch to discuss your needs.