Coming soon

Molecular simulation toolkit
by BiosimAI

Physicochemical simulations and AI for interrogating biology at the atomistic scale.
Develop safe, effective drugs faster.

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Toolkit for in silico
drug discovery

The BiosimAI toolkit enables scientists to interrogate biology from individual atoms to whole cells using biological simulations, biophysics, and machine learning. With our toolkit, scientists can develop safe, effective drugs faster.

Our toolkit includes:

Atomistic ligand docking
Molecular properties AI: solubility, toxicity, etc.
Atomistic molecular dynamics

The toolkit is available through Jupyter notebooks in BiosimAI software blueprints on the Deep Origin cloud platform.


Adverse, off-target effects of compounds

Protein-ligand binding

How compounds bind proteins and how strongly they bind

Solubility & distribution

Ability of compounds to circulate to target tissues

Protein structure

3D configurations of polypeptides and complexes

Biophysical simulation

Simulation of how physicochemical forces drive biological behavior"

Machine Learning

Data-driven simulation of behaviors, from individual molecules to cells

Ligand docking and molecular properties

Docking places candidate molecules within a protein's binding pocket and estimates their binding affinities. By integrating physics and AI, our algorithms offer state-of-the-art accuracy and speed.
Dock compounds against multiple protein conformations.
Identify stable poses of compounds in binding pockets.
Estimate protein-ligand binding affinities.
Predict properties such as solubility, toxicity, and more.

Atomistic molecular dynamics

Molecular Dynamics simulations enable the study of multi-protein complexes, the discovery of hidden binding sites, and precise estimation of binding affinities between compounds and proteins.
Our tools combine physics simulation and AI, enabling:
Simulating proteins, small organic molecules, and protein-drug complexes.
Explicit solvent simulations using the AMBER and CHARMM force fields for accurately predicting protein folds.
AI-approximated quantum chemistry force field for accurately modeling small molecules.
State-of-the-art free energy perturbation (FEP) methods for accurately estimating binding affinities.
Enhanced sampling methods, such as replica and umbrella sampling, for efficiently estimating binding affinities.


virtual screening

With our docking, ADMET, and molecular dynamics, we can help you identify lead compounds by cost-effectively screening billions of molecules. Contact us to learn more.