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What is a drug discovery intern computational?

A Drug Discovery Intern in Computational Sciences applies computer-based techniques to support drug discovery efforts. This role involves using molecular modeling, machine learning, and bioinformatics to analyze chemical and biological data. Interns may assist in virtual screening, structure-based drug design, and simulations to identify potential drug candidates. They collaborate with scientists to refine computational models and improve drug development processes. Strong programming, data analysis, and scientific problem-solving skills are essential for success in this role.

What are the key skills and qualifications needed to thrive as a drug discovery intern computational, and why are they important?

To excel as a Drug Discovery Intern Computational, you need a background in biology, chemistry, or a related field, along with strong computational and analytical skills. Familiarity with programming languages like Python or R, molecular modelling software, and data analysis tools is highly beneficial. Excellent problem-solving abilities, attention to detail, and the capacity to work collaboratively are valuable soft skills in this area. These skills enable interns to effectively analyze complex biological data, contribute to ongoing projects, and support innovative drug discovery processes.

What kind of projects does a drug discovery intern computational typically work on, and how do they contribute to larger research efforts?

As a Drug Discovery Intern Computational, you can expect to work on projects such as analyzing chemical compound databases, running molecular docking simulations, or processing large biological datasets to identify promising drug candidates. You'll often collaborate with other scientists, including medicinal chemists and biologists, to interpret your computational results and refine hypotheses. Your contributions are integral to accelerating early drug development, as they help narrow down potential leads much faster than traditional laboratory methods. This role offers a hands-on introduction to both scientific research and teamwork within the collaborative environment of a drug discovery team.

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Infographic showing various Drug Discovery Intern Computational job openings in the United States as of August 2026, with employment types broken down into 50% Full Time, and 50% Part Time. Highlights an 50% In-person, and 50% Hybrid job distribution.

Senior AI Scientist Drug Discovery & Computational Biology

Vytwo

Prosper, TX • On-site

$83K - $114K/yr

Full-time

Posted yesterday

New

Job description


Position Title | Senior AI Scientist – Drug Discovery & Computational Biology
Domain EXP | Healthcare
Location | Boston, MA - Hybrid
Duration | C2C
Must Have  | Drug Discovery & Computational Biology
Job Description
We are seeking a highly skilled professional with deep expertise in applying Artificial Intelligence and Machine Learning to drug discovery and development. The ideal candidate will have experience in: ·                AI and machine learning methodologies for drug discovery, including predictive modeling, lead optimization, and translational research applications.·                Graph machine learning techniques for biological networks, molecular property prediction, target identification, and knowledge graph-based discovery.·                Virtual cell screening and AI-driven target discovery platforms, with particular focus on rare disease research and therapeutic innovation.·                AI-driven molecular design for small molecules and biologics (Abs/VHH), including toxicity prediction, off-target assessment, developability analysis, and candidate optimization.·                Structural biology, including protein structure analysis, molecular interactions, computational modeling, and integration of structural data into drug discovery workflows. The candidate should demonstrate a strong track record of leveraging advanced computational approaches to accelerate therapeutic discovery and possess the ability to collaborate effectively with multidisciplinary research and development teams.