Technology

An integrated AI platform
from hit to formulation.

Our workflow connects AI-guided discovery, computational developability assessment, and translational decision-making into a single, unified platform.

PILLAR 01

AI-Guided Discovery.

Transforming millions of compounds into a decision-ready shortlist.

Our discovery engine combines computational chemistry with modern machine learning to evaluate vast chemical spaces, efficiently, accurately, and reproducibly.

  • Cloud-based virtual screeningat scale, with elastic compute for large libraries.
  • Molecular dockingwith high-accuracy scoring functions and pose evaluation.
  • QSAR modeling(Quantitative Structure-Activity Relationship) for predictive bioactivity.
  • ADMET predictionfor early developability and toxicity flags.
  • Multi-criteria hit prioritizationfocusing experimental resources where they matter most.
Tools & Frameworks GNINA · DeepChem · KNIME · Custom ML pipelines
AI-guided discovery: library preparation, receptor validation, ML classification, GNINA docking and drug-likeness
PILLAR 02

AI-Assisted Formulation.

Formulation thinking, integrated from day one.

Most discovery platforms hand off "hits" with no consideration for downstream development. We embed formulation feasibilty directly into the discovery process dramatically reducing late-stage attrition.

  • Computational developability assessmentfor every shortlisted candidate.
  • Nanoformulation strategyguided by AI-predicted physicochemical properties.
  • Solubility, stability, and bioavailabilityprediction at the molecular level.
  • Early identification of formulation red flagsbefore resource commitment.
Capabilities Developability scoring · Excipient selection · Delivery modeling
AI-guided nanoformulation — predicting quality attributes