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Project: Deep Generative Models for (Conditional) Molecule Generation


Project description:

Generative AI has become one of the leading approaches to (conditional) molecule generation. Like Large Language Models can learn (to some degree) rules governing natural language, could Large Chemistry Models learn rules governing atoms (quantum chemistry)? This is the leading research question of this project. To answer this question, we will look into various representations of molecules and deep generative models suitable for them. We will consider at least one of the problems in (conditional) molecule generation, e.g., de novo drug design, lead optimization, structure-based molecule generation.

In this thesis: (a) you will study the techniques for formulating molecule generation models, (b) you will formulate and code your molecule generation models, (c) you will design and carry out evaluations for your molecule generation models.

Literature (examples):


  • reading and understanding scientific literature
  • very good coding skills in Python using PyTorch and other ML libraries
  • good knowledge of Deep Learning and the basics of Generative AI
  • curious attitude, independence, thinking out-of-the box
Jakub Tomczak
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