Senior Computational Drug Discovery Scientist

Location: Bengaluru

About QpiAI:

Role Overview
QpiAI Pharma is an AI-driven computational drug discovery platform integrating cheminformatics, structural biology, systems biology, machine learning, quantum computing, and scalable scientific workflows to accelerate the identification of novel therapeutics.
We are seeking Senior Computational Drug Discovery Scientists to design, implement, validate, and product ionize core scientific workflows for both small molecule and target discovery programs. This role requires deep scientific expertise combined with platform-oriented systems thinking to translate research into production-grade pipelines.

Key Responsibilities
1. Small Molecule Drug Design Workflows (LBDD/SBDD)
Lead and implement workflows for  de novo  design, virtual screening, molecular docking, molecular dynamics (MD) simulations, FEP, QSAR/QSPR, and pharmacophore modeling, leveraging open-source packages (Auto Dock Vina, GROMACS, LAMMPS, deep-learning accelerated solvers).
Design, validate, and product ionize screening pipelines for hit identification, ADMET prediction, and lead optimization.
Conduct Structure-Activity Relationship (SAR) analysis, chemical space exploration, and property optimization using computational methods.
Establish standards for compound library curation and high-quality assay data standardization for model training.
2. Target and Systems Biology Discovery
Drive novel target identification and validation using multi-omics data (genomics, proteomics, transcriptomics), network biology, and literature mining.
Perform pathway analysis and deep disease biology interpretation to generate mechanism-level hypotheses for drug programs.
Build scalable, production-ready workflows for biological data integration and quantitative target prioritization.
3. Platform Integration and ML/AI Application
Design modular, reproducible computational chemistry and biology workflows.
Collaborate with software teams to product ionize and scale pipelines on cloud/HPC.
Integrate deep learning models with model tracking, versioning, and deployment.
Support development and/or integration of agentic workflows into platforms.
Contribute to scientific documentation and intellectual property (IP) generation.

Required Technical Skills
Strong expertise in at least one core area of computational drug discovery:
Ligand-Based Drug Design (LBDD) and/or Structure-Based Drug Design (SBDD)
Computational Structural Biology (MD, FEP, docking, with proficiency in tools like GROMACS, AMBER, NAMD, and Desmond)
Computational Systems Biology / Target Discovery
Hands-on experience with force field-based simulations.
Direct exposure to implementing and utilizing deep learning in drug discovery, including generative models.
Advanced proficiency in Python programming (RDKit, Num Py, PyTorch)
Proven experience building and optimizing scalable computational scientific pipelines.
Solid understanding of the end-to-end small molecule drug discovery decision process.
Strong knowledge of best practices in benchmarking, reproducibility, and rigorous scientific validation.

Preferred Skills
Track record of contributions to research publications, patent applications, or invention disclosures.

Educational Qualifications
PhD in one of the following (mandatory):
Computational Chemistry
Cheminformatics
Computational Biology
Drug Discovery
Systems Biology
Related quantitative life sciences discipline

Experience Requirements
2–5 years of relevant post-PhD experience in computational drug discovery/design and platform development or related fields.
Demonstrated experience building end-to-end, high-throughput computational screening and analysis workflows.
Experience working with complex, real-world biological and chemical datasets  in a pharmaceutical or biotech context .
Proven exposure to interdisciplinary collaboration.