Generative AI for Drug Discovery

About this course

This course explores how generative artificial intelligence is transforming de novo drug design and small molecules discovery. It introduces the fundamental principles behind generative AI-driven molecule generation using basic Deep Learning architectures such as Long Short Term Memory (LSTM) / / GRU (Gated Recurrent Units) trained on chemical representations like SMILES. Participants will learn how Long Short Term Memory models enable the creation of novel, synthetically feasible compounds with optimized physio chemical and pharmaco kinetic properties.

A key focus of this course will be on target specific fine tuning of the pre trained models where we will use active learning strategies that iteratively guide the model to generate molecules with desired pharmacological profile training based on physics based calculations such as structure–activity relationships. Through hands-on exercises, students will apply techniques that integrate molecular docking, property prediction, and active learning to refine compound generation toward specific therapeutic targets. By the end of the course, participants will gain practical experience in building a generative AI model for de novo drug design from the scratch and sequential fine tuning of the pre trained models for target specific molecule generation with desired pharmacological profile implemented in real-world drug discovery workflows, bridging the gap between computational design and experimental validation.

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