Job Description

Job Responsibilities

1. Generative AI-Driven Molecular Design: Design novel drug molecular structures with specific biological activities using generative models or AI technologies.
2. Computational Binding Optimization: Predict and optimize interactions between potential drug compounds and targets by integrating molecular biology with computational chemistry simulations (e.g., protein-ligand docking, molecular dynamics).
3. Virtual Screening Platform Development: Build virtual high-throughput screening platforms combining activity prediction and pharmacophore modeling to iteratively optimize molecular activity, ultimately finalizing candidate molecules with high drug-like properties and synthetic feasibility.

Qualifications

1. Education & Experience:

• Ph.D. in Computational Medicinal Chemistry, Computer Science, Pharmaceutical Chemistry, or related field, specializing in AI/ML applications in life sciences; minimum 3 years of relevant experience.

1. Technical Expertise:

• Foundational Computational Layer:
• Proficient in computational methods for drug discovery: protein homology modeling, protein structure prediction, quantum chemistry calculations, molecular dynamics (MD) simulations, molecular docking, and molecular toxicity prediction.
• Capable of de novo drug design based on target protein structural features.
• AI Algorithm Layer:
• Experienced in AI-driven drug design: molecular construction/optimization using AI, enhancing interaction prediction accuracy via graph neural networks (GNNs), and applying free energy perturbation (FEP) to validate binding modes.
• Medicinal Chemistry Translation Layer:
• Proven ability to collaborate with synthetic and biochemistry teams to ensure designed molecules meet dual requirements of synthetic accessibility and drug-likeness.
• Skilled in establishing computational design → synthetic validation closed loops (e.g., interpreting biological assay data, applying Lipinski's Rule of Five, scaffold hopping for molecular optimization).
• Experience in this layer is preferred.

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