Principal Scientist I, Computational Drug Discovery Lead (EDDC A*STAR)

A*STAR - Agency for Science, Technology and Research; Singapore; On-site; SGD 300,000 - 420,000; Full time

Job summary

A*STAR in Singapore seeks a hands-on scientific leader to build and lead a Computational Drug Discovery function. You will create an integrated platform spanning target identification, hit discovery, lead optimization, DMPK, and candidate selection.

The role sits at the intersection of computational chemistry, structural biology, bioinformatics, physics-based modeling, and AI/ML with focus on small molecule discovery and degradation biology.

Qualifications

• Ph.D. in Computational Chemistry, Biophysics, Structural Biology, Chemistry, Bioinformatics, Computational Biology, or related field.
• 10+ years of industry experience, including scientific leadership and team building.
• Strong publication record or demonstrated impact on drug discovery programs.

Responsibilities

• Define and execute computational drug discovery strategy across small molecule programs, integrating AI from target identification to candidate selection.
• Build AI-enabled workflows for hit discovery, hit-to-lead, lead optimization, and DMPK prediction.
• Integrate molecular, structural, omics, functional data to inform target selection and mechanism of action.
• Lead modeling of ternary complexes, cooperativity, and degradation efficiency using structural and computational data.
• Apply MD, FEP, enhanced sampling, docking, QM/MM with AI/ML methods for structure prediction and scoring.
• Build and mentor a multidisciplinary team of computational chemists, modelers, and data scientists.
• Partner with chemistry, biology, structural biology, and DMPK teams to generate actionable insights.
• Evaluate emerging technologies and represent the function in project teams, reviews, collaborations, and planning.

Skills

Structural biology

AI/ML methods

Molecular design

Physics-based modeling

Python

Education

Ph.D. in Computational Chemistry/related field

Tools

Schrödinger

OpenEye

RDKit

Job description

Position Overview

AI-driven drug discovery is a strategic priority for our organization. We are seeking a hands‑on scientific leader to build and lead our Computational Drug Discovery function, creating an integrated computational platform that supports decisions from target identification and hit discovery through lead optimization, DMPK, and candidate selection.

AI-driven drug discovery is a strategic priority for our organization. We are seeking a hands‑on scientific leader to build and lead our Computational Drug Discovery function, creating an integrated computational platform that supports decisions from target identification and hit discovery through lead optimization, DMPK, and candidate selection. The role sits at the intersection of computational chemistry, structural biology, bioinformatics, physics-based modeling, and AI/ML, with a focus on small molecule discovery, induced proximity, and targeted protein degradation. The successful candidate will combine molecular modeling, structure prediction, omics and functional data analysis, machine learning, and generative AI to guide target selection, molecule design, mechanism elucidation, and therapeutic strategy in close partnership with multidisciplinary project teams.

Key Responsibilities

• Define and execute computational drug discovery strategy across small molecule programs, integrating computational chemistry, bioinformatics, and AI from target identification to candidate selection.
• Build AI-enabled workflows for hit discovery, hit-to-lead, lead optimization, DMPK prediction, and design-make-test-analyze cycles.
• Integrate molecular, structural, omics, functional screening, and clinical datasets to inform target selection, patient stratification, mechanism of action, and therapeutic opportunity assessment.
• Lead modeling of ternary complexes, cooperativity, and degradation efficiency using structural, biophysical, and computational data.
• Apply physics-based methods such as MD, FEP, enhanced sampling, docking, and QM/MM alongside AI/ML methods including structure prediction, generative chemistry, predictive modeling, and ML-based scoring.
• Build and mentor a multidisciplinary team of computational chemists, modelers, and data scientists.
• Partner closely with chemistry, biology, structural biology, and DMPK teams to generate actionable insights and advance programs.
• Evaluate emerging technologies and represent the function in project teams, portfolio reviews, external collaborations, publications, and resource planning.

The Requirement

• Ph.D. in Computational Chemistry, Biophysics, Structural Biology, Chemistry, Bioinformatics, Computational Biology, or related field. 10+ years of industry experience, including scientific leadership and team building.
• Strong expertise in structural biology, physics-based modeling, molecular design, and modern AI/ML methods.
• Proven ability to build AI-enabled workflows that influence discovery decisions at program, portfolio, or organizational level.
• Experience integrating omics, functional, or translational datasets into target identification, mechanism studies, biomarker discovery, or patient stratification.
• Knowledge of targeted protein degradation, including ternary complex modeling, cooperativity, E3 ligase biology, and PROTAC or molecular glue SAR.
• Proficiency in scientific programming and data analysis, including Python, RDKit, and molecular modeling platforms such as Schrödinger, OpenEye, or equivalents.
• Strong publication record or demonstrated impact on drug discovery programs, with excellent cross‑functional communication skills.

Preferred Qualifications

• Experience advancing degrader or targeted protein degradation programs from hit identification to candidate selection.
• Experience scaling computational capabilities and driving adoption of AI/ML tools and platforms.