At thinkMolecular, we endeavour to accelerate drug discovery through the power of physics-based molecular simulations and AI-driven insight. Comprising a diverse team of scientists, engineers, and thinkers with deep expertise in computational chemistry, biophysics, structural biology, and machine learning, our mission is to understand biology and chemistry at the molecular level and use in-silico experiments to help better predict and design the drugs of tomorrow. Our machine learning augmented molecular simulations enrich the traditional design-make-test cycle in the drug discovery campaigns and transform it through our predict first approach using data analysis-technology-expertise.
Based out of Bengaluru in India, thinkMolecular was founded in the year 2020 by Dr. Ramesh Sistla, a recognized leader in the field of drug design. Ever since, we have delivered projects for more than twenty partners and clients. Apart from lead optimization campaigns of small molecules in diverse therapeutic areas and target classes, thinkMolecular has contributed to radio ligand design, protein engineering, probe design, target validation, RNA drug discovery, DEL screening, library design, library selection and asset due diligence to name a few. Our broad ranging and ever growing technology repertoire along with deep experience ensures that we are able to support diverse projects that our partners and clients offer.
Reach out to info@thinkmolecular.in to ask for specific case studies you may be interested in.
This chatGPT paraphrasing about us is accurate: At thinkMolecular, we don’t just simulate molecules. We simulate the future of drug discovery.
Expertise
Science driven testable hypotheses for drug discovery- Experience across target classes and therapeutic areas
- Any target class, any MoA
Efficiency
- AI and ML augmented physics based modeling
- Data analysis with actionable output for prospective designs
- Solutions across the drug discovery chain
Enabling
- Bring efficiency to drug discovery
- Enrich and enable experimentalist with right input
- Enable early and appropriate decision making in your discovery through testable hypotheses