MedGraphR
AI-automated Platform for Rapid & Precise Drug Design

Our user-friendly platform empowers you to automate, plan and seamlessly oversee all facets of multiple early stage small molecule drug discovery and drug repurposing projects, all in one place

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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.

Small Molecule - NCE

AI-driven inverse design of molecules with desired pharmacological properties in an automated fashion.

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    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.

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    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.

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    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

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    4.
    Virtual Screening

    A) MOLECULAR DOCKING

    Molecular docking reveals crucial interaction mechanisms between the candidate drug and target pocket residues responsible for biological activity.

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    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.

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    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



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