Computational Chemist for Drug Discovery

Overview:

Molecule AI, an innovative AI-based drug discovery company, seeks a Computational Scientist skilled in molecular modelling, molecular dynamics (MD) simulations, and machine learning for drug discovery. This role involves developing and evaluating computational methods for molecular property prediction, utilising molecular modelling for drug discovery, and working with AI models for de novo drug design. Relevant areas of experience include the direct application of MD to drug discovery, computer-aided drug design, molecular modelling and medicinal chemistry. Specific knowledge of any of these areas is less critical, and higher weightage will be placed on solid research skills and a history of innovation and accomplishment.

Qualifications:

• Ph.D./M.Sc. from a reputed institution or university  in a related field with a focus on drug discovery. Post-doctoral experience is desirable but not mandatory.
• Expertise in molecular modelling, MD simulations, and free energy calculations.
• Proficient in Python.
• Strong problem-solving and communication skills.

Key Responsibilities: Computational Chemist in Drug Discovery

1. Molecular Modeling & Drug Design

• Conduct molecular docking (AutoDock, Unidock) to predict binding modes of small molecules.
• Perform virtual screening of compound libraries to identify potential hits.
• Use molecular dynamics (MD) simulations (GROMACS, AMBER, etc) to study protein-ligand interactions.
• Perform structure-based drug design (SBDD) and ligand-based drug design (LBDD).
• Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties using machine learning algorithms & AI-based approaches.

2. Cheminformatics & Data Analysis

• Analyze structure-activity relationships (SAR) to guide medicinal chemistry efforts.
• Apply machine learning (ML) & AI-based approaches for predictive modeling (QSAR, deep learning).
• Work with large chemical databases (ChEMBL, PubChem, ZINC) to extract relevant data.
• Develop pharmacophore models to identify key molecular features for activity.

3. Protein Modeling & Bioinformatics

• Perform homology modeling for proteins with unknown structures.
• Predict protein-ligand & protein-protein interactions.
• Analyze protein dynamics & conformational changes relevant to drug binding.

4. Collaboration with Experimental Teams

• Work closely with medicinal chemists to optimize lead compounds.
• Support biologists & pharmacologists in interpreting biochemical assay data.
• Validate computational predictions with experimental results (X-ray crystallography, NMR, biochemical assays).

5. Software & Tools Development

• Develop custom scripts (Python, & others) for data analysis & automation.
• Use quantum mechanics/molecular mechanics (QM/MM) for detailed electronic structure analysis.
• Stay updated with emerging computational tools (AlphaFold, DeepChem, DiffDock).

6. Documentation & Reporting

• Prepare technical reports & presentations for stakeholders.
• Maintain detailed records of computational experiments.
• Contribute to scientific publications and patent applications.

The right candidate will have 2-5 years of industrial work experience and strong coding ability. 

**Interested candidates can send their CVs to info@moleculeai.com or priyanka.balodi@moleculeai.net to apply for this position.