In-Silico Evaluation Suite
Computational Biology Platform for Mechanistic Insight and Safety Prediction
An AI-driven computational biology platform that simulates and evaluates drug behavior before it enters the lab. Designed to accelerate preclinical decision-making, it integrates molecular modeling, dynamics simulations, pharmaco-kinetic estimation, and safety profiling, allowing researchers to assess mechanistic plausibility, efficacy potential, and toxicity risk entirely in silico.
Pharmaceutical R&D
Rapid triage of candidate compounds before animal studies.
Rapid triage of candidate compounds before animal studies.
Biotech Startups
De-risk early-stages with computational safety and efficacy evidence.
De-risk early-stages with computational safety and efficacy evidence.
CROs & Research Labs
Integrate predictive modeling into existing workflows
Integrate predictive modeling into existing workflows
Investors & IP Firms
Scientifically validate repurposed or acquired assets before licensing.
Scientifically validate repurposed or acquired assets before licensing.
- Structure Prediction & Interaction Modeling
Leverages next-generation protein structure predictors and ligand docking algorithms to model molecular interactions with atomic-level precision. Integrates AlphaFold-derived embeddings and custom generative models for conformational exploration.
- Molecular Dynamics & Binding Estimation
Performs molecular dynamics (MD) simulations to analyze binding stability, conformational changes, and thermodynamic profiles. AI-driven binding energy predictors provide fast, scalable affinity scoring for large compound libraries.
- Immunogenicity & PK/PD Modeling
Evaluates immunogenic potential of biologics and peptide drugs using epitope recognition algorithms. Generates PK/PD proxy models to estimate in vivo behavior – including half-life, bioavailability, and dosage viability.
- Mechanistic Plausibility Engine
Cross-references results with known biological pathways and disease ontologies to validate mechanistic relevance. Generates a Mechanistic Confidence Score, helping researchers prioritize assets with the strongest biological rationale.
Comprehensive Mechanistic Coverage
From target binding to clinical viability
From target binding to clinical viability
Rapid Decision-Making
Reduces preclinical evaluation time from months to days
Reduces preclinical evaluation time from months to days
High-Throughput Scalability
Analyze thousands of assets in parallel
Analyze thousands of assets in parallel
Cross-Validation with Real-World Data
Ensures reliability across therapeutic domains
Ensures reliability across therapeutic domains
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