Drug Discovery Research Intern | M.Pharm Pharmacology + M.Sc Biotechnology | M.Pharm Pharmaceutical Chemistry

Job Title: Research Intern – AI-Driven Drug Discovery
Company Description
PSPM Technologies Pvt Ltd is a technology services company founded in 2020 and headquartered in Pune, Maharashtra, India. The company delivers web and mobile applications, enterprise software, Generative AI solutions, and technology services across multiple industries.
Our AI-driven drug discovery initiative combines pharmaceutical science, bioinformatics, and software engineering to develop computational research workflows and proof-of-concept platforms. Areas of focus include molecular docking, virtual screening, protein and ligand preparation, molecular property analysis, and interpretation of predicted ADMET properties.
Role Description
We are seeking motivated research interns pursuing or holding qualifications in Pharmaceutical Chemistry, Pharmacology, Bioinformatics, or Biotechnology to support our AI-driven drug discovery projects.
Interns will collaborate with research, AI, and software development teams to investigate therapeutic targets, analyze chemical and biological data, evaluate computational findings, and prepare scientific documentation. Responsibilities will be assigned according to each candidate’s academic background, research interests, and practical skills.
This is a remote, unpaid internship for a duration of six months, offering practical exposure to computational drug discovery research and interdisciplinary collaboration.
Key Responsibilities
Conduct structured literature reviews on disease mechanisms, therapeutic targets, existing drugs, and potential drug candidates.
Assist with protein structure assessment and preparation for molecular docking studies.
Support ligand preparation, molecular structure checks, and compound library organization.
Assist with molecular docking and virtual screening under research guidance.
Analyze docking poses, protein–ligand interactions, and molecular properties.
Review biological pathways, mechanisms of action, and the biological plausibility of shortlisted compounds.
Support the interpretation of predicted absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties.
Compare computational findings with published research and reference compounds, documenting limitations and evidence gaps.
Organize research datasets, references, methods, parameters, and results to support reproducible workflows.
Prepare scientific summaries, technical reports, presentations, and project documentation.
Collaborate with AI and software teams to define scientific requirements and review the consistency and usability of platform outputs.
Participate in remote team meetings and present research progress, findings, and questions.
Qualifications
Candidates should be pursuing or have completed one of the following qualifications:
M.Pharm in Pharmaceutical Chemistry or Medicinal Chemistry.
M.Pharm in Pharmacology.
M.Sc in Bioinformatics.
M.Sc in Biotechnology.
A closely related postgraduate discipline with relevant drug discovery research experience.
Candidates need a strong foundation in their own discipline; expertise across all four backgrounds is not required.
Required Skills
Relevant knowledge of medicinal chemistry, pharmacology, structural bioinformatics, molecular biology, or biotechnology.
Ability to read scientific publications, critically evaluate evidence, and prepare clear research summaries.
Interest in computational research and AI/ML applications in drug discovery.
Awareness of the limitations of docking scores and ADMET predictions, including the need for experimental validation.
Strong written and verbal communication skills.
Attention to detail and consistent scientific documentation.
Ability to manage assigned tasks independently and collaborate effectively in a remote setting.
Commitment to research integrity, accurate reporting, and continuous learning.
Preferred Skills
The following skills are advantageous but not mandatory:
Academic projects or research experience involving molecular docking, virtual screening, target identification, or ADMET analysis.
Familiarity with AutoDock Vina, PyMOL, RDKit, or comparable computational research tools.
Experience using scientific databases such as PDB, PubChem, ChEMBL, or UniProt.
Basic Python or R skills for scientific data analysis.
Familiarity with protein structures, biological sequence analysis, cheminformatics, or molecular interactions.

Work arrangement
No