Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space. Recent years have witnessed promising progress for deep learning in DTI predictions. However, the following challenges are still open: (1) the sole data-driven molecular representation learning approaches ignore the sub-structural nature of DTI, thus produce results that are less accurate and difficult to explain; (2) existing methods focus on limited labeled data while ignoring the value of massive unlabelled molecular data. We propose a Molecular Interaction Transformer (MolTrans) to address these limitations via: (1) knowledge inspired sub-structural pattern mining algorithm and interaction modeling module for more accurate and interpretable DTI prediction; (2) an augmented transformer encoder to better extract and capture the semantic relations among substructures extracted from massive unlabeled biomedical data. We evaluate MolTrans on real world data and show it improved DTI prediction performance compared to state-of-the-art baselines.
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning
Tsinghua University（清华大学）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；Renmin University of China（中国人民大学）；University of Pisa（比萨大学）
Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer
Sun Yat-sen University（中山大学）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）；Microsoft Research Asia（微软亚洲研究院）；Fudan University（复旦大学）
ImageDDI: Image-enhanced Molecular Motif Sequence Representation for Drug-Drug Interaction Prediction
College of Computer science and Electronic Engineering, Hunan University（计算机科学与电子工程学院，湖南大学）；Department of Integrative Biotechnology, Yonsei University（整合生物技术系，延世大学）
Associative Learning Mechanism for Drug-Target Interaction Prediction
Chongqing University of Posts and Telecommunications（重庆邮电大学）；State University of New York at Buffalo State（纽约州立大学布法罗州立学院）；DH (Shanghai) Diagnostics Co.,Ltd.（DH（上海）诊断有限公司）
TCR: A Transformer Based Deep Network for Predicting Cancer Drugs Response
Baidu（百度）
由于肿瘤的异质性，在个性化的基础上预测抗癌药物的临床结果在癌症治疗中具有挑战性。传统的计算工作是通过分子谱来描述药物对个体样本的影响，但由于组学数据的高维性，会出现过拟合，阻碍了模型的临床应用。最近的研究表明，深度学习是一种很有前途的方法，通过学习药物和样本之间的比对模式来建立药物反应模型。然而，现有的研究采用简单的特征融合策略，只考虑作为一个整体表示的药物特征，而忽略了子结构信息，可能发挥重要作用时，比对药物和基因。因此，在本文中，我们提出TCR（Transformer based network for Cancer drug Response）来预测抗癌药物的反应。通过利用注意力机制，TCR能够有效地学习药物原子/亚结构与分子特征之间的相互作用。此外，设计了双损失函数和交叉采样策略，以提高TCR的预测能力。我们表明，TCR优于所有其他方法下的各种数据分割策略的所有评估矩阵（一些显着改善）。大量的实验表明，TCR在独立的体外实验和体内真实患者数据上显示出显着提高的泛化能力。我们的研究强调了TCR的预测能力及其对癌症药物再利用和精确肿瘤治疗的潜在价值。
Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks
Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University（哈切塔佩大学计算机工程系生物数据科学实验室）；Dept. of Computer Engineering, Hacettepe University（哈切塔佩大学计算机工程系）；Dept. of Chemistry, Middle East Technical University（中东技术大学化学系）；Dept. of Physics, Middle East Technical University（中东技术大学物理系）；Dept. Of Artificial Intelligence Engineering, Bahcesehir University（巴切塞希尔大学人工智能工程系）；Cancer Systems Biology Lab, Graduate School of Informatics, Middle East Technical University（中东技术大学信息研究生院癌症系统生物学实验室）；Dept. of Pharmaceutical Chemistry, Faculty of Pharmacy, Gazi University（加齐大学药学系药剂化学系）；Laboratory of Molecular Modeling, Evias Pharmaceutical R&D Ltd.（Evias制药研发有限公司分子建模实验室）；Dept. of Electrical and Electronics Engineering, İskenderun Technical University（伊斯肯德伦技术大学电子与电气工程系）；Institute for Computational Biomedicine, Heidelberg University（海德堡大学计算生物医学研究所）；Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University（哈切塔佩大学健康科学研究生院生物信息学系）
A systematic investigation of molecular encoding methods for drug property predictions across neural network and Transformer encoder-based model
School of Medicine, National Tsing Hua University（国立清华大学医学院）；Institute of Bioinformatics and Structural Biology, National Tsing Hua University（国立清华大学生物信息学与结构生物学研究所）；Department of Life Science, National Tsing Hua University（国立清华大学生命科学系）；Interdisciplinary Program of Life Sciences and Medicine, National Tsing Hua University（国立清华大学生命科学与医学跨学科计划）
Contextual Molecule Representation Learning from Chemical Reaction Knowledge
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；University of Copenhagen（哥本哈根大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）
Benchmark on Drug Target Interaction Modeling from a Drug Structure Perspective
University of Minnesota（明尼苏达大学）；University of California San Diego（加州大学圣地亚哥分校）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）；North Carolina State University（北卡罗来纳州立大学）
MolCAP: Molecular Chemical reActivity pretraining and prompted-finetuning enhanced molecular representation learning
School of Software, Shandong University（山东大学软件学院）；Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University（山东大学联合SDU-NTU人工智能研究中心（C-FAIR））
GastroDL-Fusion: A Dual-Modal Deep Learning Framework Integrating Protein-Ligand Complexes and Gene Sequences for Gastrointestinal Disease Drug Discovery
Technical University of Munich（慕尼黑工业大学）；University of Michigan（密歇根大学）；Georgia Institute of Technology（佐治亚理工学院）
StateXDiff: Cell State-Contextualized Multimodal Diffusion for Single-Cell Perturbation Prediction
College of Future Technology（未来技术学院）；College of Informatics, Huazhong Agricultural University（华中农业大学信息学院）；Department of Clinical Neurosciences, University of Cambridge（剑桥大学临床神经科学系）
