Deep learning is the predominant machine learning paradigm in language processing. This approach not only gave huge performance improvements across a large variety of natural language processing tasks. This time we will loke at agentic AI for lifes science and other papers on drug discovery
Lecturer: Dietrich Klakow
Location: t.d.b
Time: block course in the spring break 2027 however preparations start earlier. Here the specific time line
Closing topic doodle: tbd
Kick-Off: tbd
One page outline: tbd
Draft presentation: tbd
Practice talks and final talks will be during the spring break. Time/date will be decided during the kick-off.
Application for participation: see CS seminar system (for CS; BioInf, DSAI, VC, ES, …), for CoLI,LST, LCT use LSF to apply.
LSF exam registration deadline: 29.1.27
Grading (tentative):
- 5% three slide/5 min presentation
- 10% draft of full presentation
- 10% practice talk
- 25% own experiments and coding
- 10% report on coding task
- 35% final talk
- 5% contributions to discussion during final talk of fellow participants
List of Topics (tentative):
- Augmenting large language models with chemistry tools
- Autonomous chemical research with large language models
- LIDDIA: Language-based Intelligent Drug Discovery Agent
- DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration
- Democratising real-world drug discovery through agentic AI
- Towards an AI co-scientist
- AI agents in drug discovery: applications and case studies
- Towards end-to-end automation of AI research
- DeCoDe: Decoupling Binding Position and Molecular Conformation in 3D Ligand Diffusion for Structure-Based Drug Design
- From Feasible to Practical: Pareto-Optimal Synthesis Planning
- Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Design
- Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics