BioDynLab provides a physics‑based computational platform, OPTIMUS™, that quantifies dynamic molecular complexity to prioritize atoms and moieties driving biological function during lead optimization. The tool evaluates large analog series without training data, integrates easily with existing cheminformatics workflows, and supports rapid target assessment for data‑sparse therapeutic areas such as orphan and rare diseases.
Funding
Funding not disclosed
Founders
Product
Problem
During hit-to-lead and lead optimization stages, medicinal chemists must evaluate large series of analogs to identify which molecular modifications drive biological activity. Traditional methods rely on static structural descriptors or machine‑learning models that require extensive training data, making it difficult to prioritize compounds for novel or data‑sparse targets.
Solution
BioDynLab’s OPTIMUS™ platform applies a physics‑based, deterministic approach called Dynamic Molecular Complexity (DMC) to quantify the non‑linear dynamics of atomic motion in small molecules. By analyzing these dynamics, OPTIMUS™ pinpoints the specific atoms and moieties that contribute most to biological function without needing prior training data. The platform can process batches of analogs, delivering prioritized lists that guide synthesis decisions and accelerate lead refinement. Outputs integrate directly with existing cheminformatics pipelines, enabling seamless adoption within current drug‑discovery workflows. This deterministic, explainable methodology supports rapid deployment across novel, orphan, or rare‑disease targets where historical data are limited.
Target Audience
Primary users are medicinal chemistry and lead‑optimization teams in pharmaceutical and biotech companies seeking data‑driven, explainable methods to prioritize analog series, especially for novel or orphan disease programs.
Features
- Dynamic Molecular Complexity metric that combines physics and information theory to capture non‑linear atomic dynamics
- Deterministic, physics‑based scoring of small‑molecule candidates that identifies activity‑driving atoms and moieties
- Batch processing of analog series for scalable prioritization of synthesis candidates
- No requirement for training datasets, allowing immediate use on novel or data‑sparse targets
- Direct integration with standard cheminformatics and molecular‑design tools via compatible output formats
- Explainable results that provide clear rationale for each prioritized compound