A central task in computational drug discovery is to construct models from known active molecules to find further promising molecules for subsequent screening. However, typically only very few active molecules are known. Therefore, few-shot learning methods have the potential to improve the effectiveness of this critical phase of the drug discovery process. We introduce a new method for few-shot drug discovery. Its main idea is to enrich a molecule representation by knowledge about known context or reference molecules. Our novel concept for molecule representation enrichment is to associate molecules from both the support set and the query set with a large set of reference (context) molecules through a Modern Hopfield Network. Intuitively, this enrichment step is analogous to a human expert who would associate a given molecule with familiar molecules whose properties are known. The enrichment step reinforces and amplifies the covariance structure of the data, while simultaneously removing spurious correlations arising from the decoration of molecules. Our approach is compared with other few-shot methods for drug discovery on the FS-Mol benchmark dataset. On FS-Mol, our approach outperforms all compared methods and therefore sets a new state-of-the art for few-shot learning in drug discovery. An ablation study shows that the enrichment step of our method is the key to improve the predictive quality. In a domain shift experiment, we further demonstrate the robustness of our method. Code is available at https://github.com/ml-jku/MHNfs.
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery
College of computer science and technology, Zhejiang University（浙江大学计算机科学与技术学院）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；Zhejiang Lab（之江实验室）；ZJU-Hangzhou Global Scientific and Technological Innovation Center（浙江大学杭州国际科技创新中心）；Antai College of Economics and Management, Shanghai Jiao Tong University（上海交通大学安泰经济与管理学院）
UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery
Zhejiang University（浙江大学）；Zhejiang Laboratory（浙江实验室）；Shanghai Jiao Tong University（上海交通大学）；University of Chinese Academy of Sciences（中国科学院大学）；ZJU-Hangzhou Global Scientific and Technological Innovation Center（浙大杭州国际科创中心）
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；School of Biomedical Science, Hunan University（湖南大学生物医学科学学院）
DrugLLM: Open Large Language Model for Few-shot Molecule Generation
College of Computer Science, Sichuan University（四川大学计算机学院）；West China Hospital, Sichuan University（四川大学华西医院）；Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences（北京农林科学院信息技术研究中心）
CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models
University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
An Interpretable Framework Applying Protein Words to Predict Protein-Small Molecule Complementary Pairing Rules
MOE Key Laboratory of Bioinformatics（教育部生物信息学重点实验室）；State Key Laboratory of Molecular Oncology（分子肿瘤学国家重点实验室）；Beijing Frontier Research Center for Biological Structure（北京前沿生物结构研究中心）；School of Pharmaceutical Sciences, Tsinghua University（清华大学药学院）
Budget-Sensitive Discovery Scoring: A Formally Verified Framework for Evaluating AI-Guided Scientific Selection
Indian Institute of Information Technology Allahabad (IIITA)（印度阿瓦德理工学院信息学院（IIITA））；National Institute of Electronics & Information Technology (NIELIT)（国家电子与信息科技研究所（NIELIT））
Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge
National Research Council Canada（加拿大国家研究委员会）；University of Ottawa（渥太华大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Brock University（布鲁克大学）
Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
New Laboratory of Pattern Recognition (NLPR)（新模式识别实验室（NLPR））；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS)（多模态人工智能系统国家重点实验室（MAIS））；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所（CASIA））；University of Science and Technology of China（中国科学技术大学）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction
Johannes Kepler University Linz（约翰内斯·开普勒大学林茨分校）；Institute of Advanced Research in Artificial Intelligence (IARAI)（人工智能高级研究所）；Janssen Research & Development, Janssen Pharmaceutica N.V.（杨森制药有限公司杨森研发部）；Microsoft Research（微软研究院）
