MedGraph - Oopal
Binding free energy calculations using advanced FEP and thermodynamic integration for drug discovery and lead optimization.
Overview
MedGraph Oopal™ by Medvolt.ai is an advanced physics-based Free Energy Perturbation (FEP) platform designed to deliver highly accurate binding free energy calculations for drug discovery and optimization. Built on proprietary technology and powered by cutting-edge NVIDIA GPUs, it is purpose-built for pharmaceutical and biotech teams seeking to accelerate Hit Identification, Hit-to-Lead progression, and Lead Optimization with greater precision and reduced cost.
Medvolt's platform sets a best-in-class standard through advanced alchemical calculations, in-house FEP modules, and seamless support for complex molecular system setups. By leveraging GPU-accelerated computation, MedGraph Oopal™ significantly reduces simulation time while maintaining the rigorous accuracy required at every stage of early drug discovery.
FEP-TI: Accurate Free Energy Calculations for Hit ID and Hit-to-Lead
- Integrated Approach: Combines Free Energy Perturbation (FEP) with Thermodynamic Integration (TI) to deliver highly precise free energy estimations.
- Stepwise Process: Utilises alchemical transformations and small, calculated perturbations to guide molecular systems, ensuring accurate free energy measurements.
- Enhanced Accuracy: Optimised for systems with varying structural conformations, providing precise insights into binding affinities and enabling absolute binding free energy calculations.
- In-Depth Insights: Delivers a thorough understanding of molecular thermodynamics to support optimised drug design across early discovery stages.
FEP-HRE: Enhanced Sampling for Lead Optimization
- Enhanced Sampling: Hamiltonian Replica Exchange (HRE) improves conformational sampling by running multiple system replicas at different energy levels, enabling broader exploration of energy landscapes and diverse molecular configurations.
- Integrated Techniques: FEP-HRE combines the incremental perturbation of FEP with the replica exchange process of HRE, allowing free energy calculations to be performed across multiple replicas for optimised accuracy.
- Improved Accuracy: Particularly valuable for complex systems with multiple energy minima, FEP-HRE provides more precise free energy estimates to ensure reliable data for lead optimization.
Applications of MedGraph Oopal™ FEP
- Hit Identification: Precise prediction of binding affinities to identify high-potential compounds and accelerate drug discovery.
- Lead Identification: Calculate binding free energies and predict potency to identify optimal lead compounds.
- Selectivity Prediction: Predict binding selectivity for target proteins versus off-target proteins.
- SAR Analysis: Understand how structural changes affect binding affinity and biological activity through Structure-Activity Relationship analysis.
- ADME Prediction: Predict pharmacokinetic properties to support and optimise drug design.
- Enzyme Inhibition Studies: Understand enzyme inhibition mechanisms and design more effective inhibitors.
- Mechanism of Action (MOA) Studies: Elucidate the molecular mechanisms of drug action and resistance.
- Protein-Ligand Binding Affinity Prediction: Accurately predict binding affinities for protein-ligand complexes.
Why Medvolt
- Accelerated Discovery: Medvolt's AI-powered platform reduces pre-clinical discovery time by 3x, lowers costs by 15x, and decreases failure risk by 25%.
- High-Throughput Data: Proprietary, gold-standard, high-throughput datasets enriched by AI and NLP solutions deliver speed, precision, and scalability.
- Expert Collaboration: An experienced, tech-driven team collaborates globally with leading pharma and biotech companies to deliver impactful and scalable solutions.
MedGraph Oopal™ is accessible via Medvolt's advanced in silico platform, which supports the full early drug discovery workflow from Hit ID through Lead Optimization. The platform is designed for global collaboration with pharmaceutical and biotech organisations seeking to integrate physics-based computational methods into their R&D pipelines.
