Researchers are exploring the use of large language models (LLMs) for small-molecule design in drug discovery. One approach involves training LLMs on synthetic tasks that generalize to complex molecular optimization problems, showing promise in surpassing larger models. Another method combines LLMs with evolutionary algorithms to achieve new state-of-the-art results on molecular optimization benchmarks, particularly in multi-property optimization scenarios. AI
IMPACT These methods could accelerate drug discovery by improving the efficiency and effectiveness of designing molecules with desired properties.
RANK_REASON The cluster contains two academic papers published on arXiv detailing novel methods for using LLMs in molecular design and optimization.
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Dopamine receptor D2
- Gotit.pub
- Hrant Khachatrian
- Hugging Face
- IArxiv
- large language models
- Molecular Ecology Notes
- Mortal Kombat II
- pain
- Practical Molecular Optimization
- Reinforcement Learning from Verifiable Rewards
- ScienceCast
- Small molecule designed to target metal binding site in the alpha2I domain inhibits integrin function.
- Structure-based lead optimization and biological evaluation of BAX direct activators as novel potential anticancer agents.
- Synthetic Task Scaling
AI-generated summary · Google Gemini · from 2 sources. How we write summaries →