InChI (International Chemical Identifier) is an open standard from IUPAC that represents molecular structures as hierarchical, layered strings optimized for database interoperability, unique identification, and web search via its hashed InChIKey.
An 831M-parameter encoder-decoder model that jointly encodes image, OCR text, and layout information through a two-stage training strategy, achieving state-of-the-art multimodal Markush structure recognition while remaining competitive on standard molecular structure recognition.
BioT5 uses SELFIES representations and separate tokenization to pre-train a unified T5 model across molecules, proteins, and text, achieving state-of-the-art results on 10 of 15 downstream tasks.
ChatDrug is a parameter-free framework that combines ChatGPT with retrieval-augmented domain feedback and iterative conversation to edit drugs across small molecules, peptides, and proteins.
ChemCrow augments GPT-4 with 18 chemistry tools to autonomously plan and execute syntheses, discover novel chromophores, and solve diverse chemical reasoning tasks.
ChemGE uses grammatical evolution over SMILES context-free grammars to generate diverse drug-like molecules in parallel, outperforming deep learning baselines in throughput and molecular diversity.
ChemLLM presents a comprehensive framework for chemistry-specific language modeling, including a 7M-sample instruction tuning dataset (ChemData), a 4,100-question benchmark (ChemBench), and a two-stage fine-tuned model that matches GPT-4 on core chemical tasks.
Introduces Coscientist, a GPT-4-driven AI system that autonomously designs and executes chemical experiments using web search, code execution, and robotic lab automation.
A systematic study of data transfer techniques (joint training, self-training, pre-training plus fine-tuning) applied to Transformer-based retrosynthesis. Pre-training on USPTO-Full followed by fine-tuning on USPTO-50K achieves the best results, improving top-1 accuracy from 35.3% to 57.4%.
DrugAssist fine-tunes Llama2-7B-Chat on over one million molecule pairs for interactive, dialogue-based molecule optimization across six molecular properties.
DrugChat is a prototype system that bridges molecular graph neural networks with large language models for interactive, multi-turn question answering about drug compounds. It trains only a lightweight linear adaptor between a frozen GNN encoder and Vicuna-13B using 143K curated QA pairs from ChEMBL and PubChem.
DrugEx v2 introduces Pareto-based multi-objective optimization and evolutionary exploration strategies into an RNN reinforcement learning framework for de novo drug design toward multiple protein targets.