Computational Chemist, AI-Driven Drug Discovery

Lilasciences; Cambridge; On-site; GBP 105,000 - 162,000; Full time

Benefits offered by this job

Equity

Comprehensive benefits

Job summary

Lila Sciences seeks a Scientist, Computational Chemistry, Drug Discovery to guide AI-driven drug discovery workflows, ensuring agent-generated plans are scientifically sensible and beneficial to live programs.

You will lead computational chemistry strategy across docking, virtual screening, SAR modeling, QSAR, and related tools, collaborating with chemists, biologists, ML researchers, and engineers to improve AI-assisted discovery loops.

Qualifications

• PhD or equivalent experience in computational chemistry, chemistry, cheminformatics, molecular modeling, biophysics, or a related field.

Responsibilities

• Monitor and review drug discovery agents' workflows for scientific validity.
• Evaluate agent-generated plans to ensure coherent optimization strategies.
• Advise on compound prioritization considering potency, developability, and feasibility.
• Define which computational tools agents should use and how outputs are interpreted.
• Lead computational chemistry strategy for live drug discovery programs when needed.
• Build or guide open-source workflows for docking, virtual screening, SAR analysis, and pharmacophore modelling.
• Apply protein-ligand binding modeling to support hypothesis generation and prioritization.
• Translate chemical judgment into practical requirements for AI tools and workflows.

Skills

Python

RDKit

Biopython

OpenMM

MDAnalysis

Agentic collaboration

Education

PhD or equivalent experience in computational chemistry

Tools

RDKit

Biopython

OpenMM

MDAnalysis

Job description

Lila Sciences seeks a Scientist, Computational Chemistry, Drug Discovery to guide AI-driven drug discovery workflows, ensuring agent-generated plans are scientifically sensible and beneficial to live programs.

You will lead computational chemistry strategy across docking, virtual screening, SAR modeling, QSAR, and related tools, collaborating with chemists, biologists, ML researchers, and engineers to improve AI-assisted discovery loops.