Principal Scientist Full-time

Molecular Design Team Member

GSK is seeking a highly motivated individual to join our Molecular Design team in Molecular Modalities Discovery. At GSK, we aim to efficiently accelerate the discovery and development of transformational medicines through the integration of advanced computational methods, predictive in silico models, and expert analyses. By applying these computational strategies, we will shape the company's future by working across all of GSK's Pharma R&D teams to develop a strategy that informs the most efficient way to deliver novel therapeutic modalities through computational design and achieve GSK's therapeutic requirements for clinically differentiable medicines. The successful applicant will collaborate with preclinical research and development business partners to impact the pipeline from target identification, modality selection, and molecule design to ultimately identify clinical candidate therapeutics that will greatly improve and impact the lives of the patients we serve.

This role is based in the US at our Upper Providence site in Collegeville, PA. 2-3 days on-site per week average presence is required.

Key Responsibilities:

• Collaborate with experimental groups to drive compound design and improve models using structure and ligand-based drug design, AI/ML, QSAR modeling, QM methods, and protein structure prediction tools
• Work with chemistry and biology team members to assimilate data from experiments, optimize those experiments and integrate that data into compound design
• Predict and evaluate targets for their probability of success to be drugged across various modalities including small molecules, oligonucleotides, protein degraders and antibody drug conjugates
• Apply AI/ML, de novo design and multi-objective optimization of tool compounds and target specific medicines
• Partner with multidisciplinary computational groups to develop and embed computational methods in visualization packages to enable experimental multi-variate analysis and data-driven decision-making

Why You?

Basic Qualifications:

• PhD in Computational Chemistry, Cheminformatics, Physics, Biophysics, Chemistry or related field
• Scientific contributions documented with publications and/or presentations
• Experience in one or more of the following: virtual screening, free energy methods, active learning, application of molecular generators, structure-based and ligand-based approaches for small molecule design, physicochemical property and ADMET optimization using ML models
• Experience in various modalities such as PROTACs, ADCs, Glues, Oligonucleotides, and covalent and non-covalent small molecules
• Expertise working in a Linux/Unix environment
• Experience writing code in one or more scripting languages (e.g., Python)

Preferred Qualifications:

• Post-doc or industrial internship
• Knowledge of the drug discovery process, for example medicinal chemistry, toxicology, DMPK, and/or screening data analysis
• Expertise with molecular simulation, enhanced sampling and/or statistical analysis
• Ability to present data in team meetings and participate in writing abstracts and publications
• Independently review and appraise scientific literature
• Experience using AI/ML methods for protein structure prediction and design.
• Strong knowledge of chemical and protein structure
• Experience interacting with multi-disciplinary matrix teams to address key goals, exhibiting excellent interpersonal skills