Axtria is seeking a visionary leader for its Drug Discovery & Computational Biology practice within the Clinical Solutions business. This role will spearhead next-generation R&D; front-end capabilities — from generative molecular design and multi-omics data integration to digital twin modeling and AI-driven portfolio decision-making. Acting as the primary Subject Matter Expert (SME), you will architect and deliver computational platforms that accelerate target identification, lead optimization, and candidate selection for pharmaceutical clients.

The position offers an unparalleled chance to shape Axtria's differentiated capabilities in one of the fastest-evolving intersections of AI and life sciences.

Core Responsibilities

Strategic Leadership

- Define and execute the vision for Drug Discovery & Computational Biology within Axtria’s Clinical Solutions COE, anchoring practice growth on generative AI, multi-omics, and digital twin capabilities.
- Supervise Associate Directors and senior scientists, ensuring delivery excellence, capability development, and a culture of scientific rigor.
- Maintain high CSAT and NPS scores to ensure customer satisfaction and long-term client relationships.

Generative Molecular Design
- Lead design and productionization of GNN- and transformer-based generative models (e.g., graph-based VAEs, diffusion models, equivariant architectures) for de novo molecular generation and lead optimization — explicitly focused on structure-activity modelling, not literature mining.
- Oversee property-conditioned generation pipelines that simultaneously optimize for potency, selectivity, synthesizability, and ADMET profiles.
- Partner with client medicinal chemistry teams to embed generative design workflows into existing discovery platforms and CRO ecosystems.

FAIR-Compliant Multi-Omics Pipelines
- Architect FAIR (Findable, Accessible, Interoperable, Reusable) multi-omics data integration platforms spanning genomics, transcriptomics, proteomics, metabolomics, and spatial biology for regulatory submissions and IND-enabling packages.
- Ensure pipeline outputs align with CDISC, HL7/FHIR, and emerging regulatory data standards, enabling seamless downstream submission workflows.
- Drive data governance, provenance tracking, and audit-readiness across multi-modal omics datasets at scale.

Predictive Toxicology & Continuous ADMET Optimization
- Build and operationalize predictive toxicology and ADMET models as a continuous feedback loop — integrating in silico predictions, experimental assay data,



and structural alerts to guide iterative lead refinement throughout the discovery cycle.
- Implement multi-endpoint ADMET scoring (absorption, distribution, metabolism, excretion, toxicity) with uncertainty quantification to support risk-stratified decision-making at each design-make-test-analyze cycle.
- Collaborate with safety and regulatory teams to ensure model outputs meet GxP and ICH M7/S9 compliance expectations.

Digital Twins & R&D; Front-End Innovation
- Design and deliver digital twin models of biological systems (disease pathways, patient populations, organ physiology) to de-risk early R&D; decisions and simulate therapeutic interventions in silico prior to wet-lab experimentation.
- Champion the integration of digital twin outputs with experimental feedback loops (DMTA cycles) to accelerate candidate progression timelines.
- Represent Axtria as a thought leader in computational drug discovery at industry conferences, client forums, and scientific publications.

Portfolio Go/No-Go AI & Probability-of-Success Modelling
- Build AI-powered portfolio decision engines that quantify probability-of-success (PoS) across pipeline assets, integrating clinical, preclinical, competitive intelligence, and biomarker data into real-time Go/No-Go recommendations.
- Develop simulation frameworks to model portfolio-level risk under resource constraints, enabling senior leadership to optimize R&D; investment allocation with quantified uncertainty.
- Translate probabilistic model outputs into executive-level dashboards and narratives that drive board-level and R&D; committee decisions.

Client Engagement & Growth
- Serve as the primary technical and scientific consultant for pharma/biotech clients, translating complex computational biology and AI needs into scalable, delivery-ready solutions.
- Build and nurture senior client relationships (Head of Computational Chemistry, CSO, VP R&D;) to expand Axtria’s drug discovery footprint.
- Propose and lead PoCs, create GTM collateral, and support pre-sales activities to accelerate business development.

Technical Skills
- Generative AI & Molecular Modelling: GNN, transformer, diffusion-based generative architectures; equivariant neural networks (e.g., SE(3)-Transformer,



DiffSBDD); SMILES/graph-based molecular representations.
- Multi-Omics & Bioinformatics: Genomics, transcriptomics (bulk/single-cell), proteomics, metabolomics pipelines; FAIR data principles; integration with regulatory submission workflows (CDISC, HL7/FHIR).
- ADMET & Predictive Toxicology: QSAR/QSPR modelling, multi-endpoint ADMET prediction, uncertainty quantification, in silico safety flagging aligned with ICH M7/S9.
- Digital Twins & Simulation: Systems biology modelling, ODE/PDE-based pathway simulation, PK/PD digital twin frameworks, DMTA cycle integration.
- Portfolio Intelligence & Decision AI: Bayesian probability-of-success models, Monte Carlo simulation for portfolio risk, multi-criteria decision analysis (MCDA), dashboard development (Tableau, Power BI, or custom).
- Cloud & Data Engineering: AWS/Azure/GCP for large-scale molecular and omics workloads; ETL pipeline design; Apache Airflow; Spark/Hadoop for high-throughput data processing.
- ML/AI Stack: PyTorch, PyTorch Geometric, RDKit, DeepChem, Hugging Face Transformers; MLOps tooling (MLflow, Weights & Biases); containerization (Docker, Kubernetes).
- Compliance & Data Security: GxP, HIPAA, GDPR compliance; 21 CFR Part 11 for computational artefacts supporting regulatory submissions.

Qualifications
- Education: Ph.D. or Master’s degree in Computational Chemistry, Cheminformatics, Bioinformatics, Computer Science, or a related quantitative life sciences discipline. Advanced degree strongly preferred.
- Experience: 15+ years in computational drug discovery or related fields, with at least 5 years in leadership roles overseeing cross-functional teams. Demonstrated track record of deploying generative molecular design, multi-omics pipelines, and AI-driven portfolio tools in pharmaceutical or biotech settings.
- Domain Expertise: Deep expertise in GNN/transformer-based molecular generation (beyond literature mining), FAIR-compliant omics data management, predictive toxicology as a continuous loop, and quantitative portfolio decision modelling.
- Regulatory Knowledge: Familiarity with IND/NDA/BLA submission requirements as they relate to computational evidence packages; ICH M7, S9, and E16 guidelines; GxP environments.
- Leadership & Communication: Strategic thinking, solution architecture, and delivery oversight. Excellent communication and stakeholder management skills, including the ability to distil complex computational outputs for C-suite and R&D; committee audiences. Proven ability to lead onshore/offshore delivery models.

📌 Director- Drug discovery (Gurugram)
🏢 Axtria - Ingenious Insights
📍 Gurugram