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Job Title:
Research Scientist
Department:Pharmacy | Medicinal Chemistry
The Computational Scientist - AI-Driven Drug Discovery will support the Buckeye BioLaunch initiative, a university-wide effort designed to accelerate therapeutic discovery, development, and commercialization through AI-enabled approaches.
The successful candidate will develop, implement, and apply computational, machine learning, cheminformatics, structural modeling, and data science approaches to identify and optimize therapeutic candidates. This individual will work closely with faculty investigators, medicinal chemists, biologists, clinicians, pharmacologists, translational scientists, and commercialization teams to advance AI-driven drug discovery projects. The position will contribute to multidisciplinary project teams supporting therapeutic programs across disease areas.
This role is expected to serve as a core scientific contributor within Buckeye BioLaunch, enabling data-driven decision-making across early discovery and translational development. The scientist will help design computational workflows, analyze large-scale biological and chemical datasets, support virtual screening and molecular design campaigns, evaluate target tractability and druggability, and contribute to the development of reproducible computational platforms that accelerate therapeutic innovation.
Required Education/Experience:
PhD or equivalent doctoral degree in computational chemistry, computational biology, cheminformatics, bioinformatics, computer science, pharmaceutical sciences, biophysics, chemical biology, biomedical engineering, statistics, data science, or a related field.
Strong background in AI/ML methods applied to biomedical, chemical, or pharmaceutical research.
Demonstrated experience in computational drug discovery, cheminformatics, molecular modeling, virtual screening, or structure-based drug design.
Desired Experience:
Experience with generative AI methods for molecular design, graph neural networks, protein language models, foundation models, active learning, Bayesian optimization, or reinforcement learning.
Experience with protein structure prediction, protein–ligand interaction modeling, AlphaFold-derived structures, cryo-EM or crystallographic data, or structural bioinformatics.
Experience with ADMET modeling, toxicology prediction, pharmacokinetics/pharmacodynamics modeling, or translational biomarker analysis.
Additional Information:Location:Pelotonia Research Center (1040)
Position Type:Regular
Scheduled Hours:40
Shift:First Shift
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