Computational Chemist Drug Discovery / Binding Free Energy

We are building a proprietary computational platform to transform lead optimization in drug discovery by replacing costly experimental screening with automated physics-based prediction of molecular properties.

What we do
Pharma teams must evaluate hundreds of molecules after AI screening, but experimental validation with synthesis and binding is slow and expensive.
Our proprietary platform predicts equilibrium constants governing binding and other molecular processes.
Output: ranked list of candidates for experimental validation (typically 5–10 lead molecules)
• Long-term target: ~80% success in candidate selection
• Potential for up to ~1000× cost reduction
• Recently automated for small molecules and proteins
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Why this is different
Existing free-energy methods (FEP / TI-based pipelines):
• Achieve ~30% success in realistic lead optimization
• Are computationally expensive and hard to scale
• Limited robustness across chemical space