MedGraph - TopaazTM
AI-automated Platform
for Rapid & Precise Drug Design
Design novel drug molecules for a target protein using our AI-driven de novo design approach. Our module combines AI-accelerated screening and physics-based modeling to identify high-potential candidates efficiently
Scalable
All our platforms are real-time, scalable products deployable in both cloud and on-premise environments. They feature a highly secure and infrastructure-agnostic architecture.
Agile
Our AI-augmented, data-intensive computing platforms significantly reduce drug discovery time from years to weeks.
MedGraph - Topaaz TM
AI-driven inverse design of molecules with desired pharmacological properties in an automated fashion.
- 1.De Novo Design
FRAGMENT-BASED MODELING
Medvolt’s extended library of fragments showing positive affinity against the target of interest is used as the base for building the compounds from scratch.
- 2.Generative AI
DEEP NEURAL NETWORKS
Our deep neural network architecture, driven by Generative AI for life sciences, uses top fragments as seeds and probabilistically generates new molecules step by step. This model rapidly creates a diverse library of active compounds in an incredibly short timeframe.
- 3.Multiparameter Optimization
REINFORCEMENT LEARNING-BASED MODELING
We optimize safety and efficacy properties through reinforcement learning. The agent can add or modify bonds or fragments in the compound, transforming it into a candidate drug capable of navigating clinical trials successfully.
- 4.Virtual Screening
A) MOLECULAR DOCKING
Molecular docking reveals crucial interaction mechanisms between the candidate drug and target pocket residues responsible for biological activity.
B) DRUG-LIKENESS PREDICTION
Our multi-task graph neural networks facilitate the transition from drug candidate to the most promising drug candidate. They compute ADME, physicochemical, and toxicity properties, validating key drug-like features.
C) MOLECULAR DYNAMICS
We provide state-of-the-art alchemical free energy calculations using molecular dynamics-FEP, coupled with HPC, to accurately predict protein-ligand binding free energy.
