MD-Syn: Synergistic drug combination prediction based on the multidimensional feature fusion method and attention mechanisms
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）
Drug combination therapies have shown promising therapeutic efficacy in complex diseases and have demonstrated the potential to reduce drug resistance. However, the huge number of possible drug combinations makes it difficult to screen them all in traditional experiments. In this study, we proposed MD-Syn, a computational framework, which is based on the multidimensional feature fusion method and multi-head attention mechanisms. Given drug pair-cell line triplets, MD-Syn considers one-dimensional and two-dimensional feature spaces simultaneously. It consists of a one-dimensional feature embedding module (1D-FEM), a two-dimensional feature embedding module (2D-FEM), and a deep neural network-based classifier for synergistic drug combination prediction. MD-Syn achieved the AUROC of 0.919 in 5-fold cross-validation, outperforming the state-of-the-art methods. Further, MD-Syn showed comparable results over two independent datasets. In addition, the multi-head attention mechanisms not only learn embeddings from different feature aspects but also focus on essential interactive feature elements, improving the interpretability of MD-Syn. In summary, MD-Syn is an interpretable framework to prioritize synergistic drug combination pairs with chemicals and cancer cell line gene expression profiles. To facilitate broader community access to this model, we have developed a web portal (https://labyeh104-2.life.nthu.edu.tw/) that enables customized predictions of drug combination synergy effects based on user-specified compounds.
Central South University（中南大学）；Nanjing Tech University（南京工业大学）；Xinjiang University（新疆大学）
Drug combination therapy has become a increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network have recently shown remarkable performance in the prediction of compound-protein interactions, but it has not been applied to the screening of drug combinations. In this paper, we proposed a deep learning model based on graph neural networks…
Mila, the Quebec AI Institute（米拉魁北克人工智能研究所）；Relation Therapeutics（Relation Therapeutics公司）；The Scripps Research Institute（斯克里普斯研究所）；Glyde Bio, Inc（Glyde生物公司）；IRIC, Institute for Research in Immunology and Cancer, Université de Montréal（蒙特利尔大学免疫学与癌症研究所（IRIC））；University of Oxford（牛津大学）；Twitter（推特）
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state of the art results in silico for the prediction of synergy scores. However, databases of drug combinations are biased towards synergistic agents and these results do not necessarily generalise out of distribution. We employ a sequential model optimization search utilising a deep learning model to quickly discover synergistic drug combinations active against a cancer …
The Chinese University of Hong Kong（香港中文大学）；BioMap (Beijing) Intelligence Technology Limited（百图生科（北京）智能技术有限公司）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；The CUHK Shenzhen Research Institute（香港中文大学深圳研究院）
Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource intensive due to the vast combinatorial search space. Recently, computational approaches, specifically deep learning models have emerged as an efficient way to discover synergistic combinations. While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines. In this study, we c…
CongFu: Conditional Graph Fusion for Drug Synergy Prediction
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；The Hospital for Sick Children（病童医院）；University Health Network（大学健康网络）；Peter Munk Cardiac Centre, University Health Network（彼得·蒙克心脏中心，大学健康网络）
Drug combinations play an important role in therapeutics due to its better efficacy and reduced toxicity. Recent approaches have applied machine learning to identify synergistic combinations for cancer, but they are not applicable to new diseases with limited combination data. Given that drug synergy is closely tied to biological targets, we propose a \emph{biological bottleneck} model that jointly learns drug-target interaction and synergy. The model consists of two parts: a drug-target interaction and target-disease association module. This design enables the model to \emph{explain} how a bi…
Washington University School of Medicine, Washington University in St. Louis（圣路易斯华盛顿大学医学院）；Dalian University of Technology（大连理工大学）
Drug resistance is still a major challenge in cancer therapy. Drug combination is expected to overcome drug resistance. However, the number of possible drug combinations is enormous, and thus it is infeasible to experimentally screen all effective drug combinations considering the limited resources. Therefore, computational models to predict and prioritize effective drug combinations is important for combinatory therapy discovery in cancer. In this study, we proposed a novel deep learning model, AuDNNsynergy, to prediction drug combinations by integrating multi-omics data and chemical structur…
Blend the Separated: Mixture of Synergistic Experts for Data-Scarcity Drug-Target Interaction Prediction
Beijing University of Posts and Telecommunications（北京邮电大学）；China Telecom Cloud Computing Research Institute（中国电信云计算研究院）；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）
TOBB University of Economics and Technology（TOBB经济与技术大学）
One of the promising methods for the treatment of complex diseases such as cancer is combinational therapy. Due to the combinatorial complexity, machine learning models can be useful in this field, where significant improvements have recently been achieved in determination of synergistic combinations. In this study, we investigate the effectiveness of different compound representations in predicting the drug synergy. On a large drug combination screen dataset, we first demonstrate the use of a promising representation that has not been used for this problem before, then we propose an ensemble …
Synergistic Benefits of Joint Molecule Generation and Property Prediction
Institute of AI for Health, Helmholtz Zentrum Munchen（人工智能与健康研究所，海德堡中心）；Technical University of Munich（慕尼黑技术大学）；School of Computation, Information and Technology（计算、信息与技术学院）；Faculty of Mathematics, Informatics and Mechanics, University of Warsaw（数学、信息学与力学学院，华沙大学）；Ardigen SA（Ardigen公司）；Eindhoven University of Technology（埃因霍温理工大学）
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）
We investigate molecular mechanisms of resistant or sensitive response of cancer drug combination therapies in an inductive and interpretable manner. Though deep learning algorithms are widely used in the drug synergy prediction problem, it is still an open problem to formulate the prediction model with biological meaning to investigate the mysterious mechanisms of synergy (MoS) for the human-AI collaboration in healthcare systems. To address the challenges, we propose a deep graph neural network, IDSP (Interpretable Deep Signaling Pathways), to incorporate the gene-gene as well as gene-drug r…
A Synergistic Framework Leveraging Autoencoders and Generative Adversarial Networks for the Synthesis of Computational Fluid Dynamics Results in Aerofoil Aerodynamics
SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）
SYNPRED: A Synergistic Approach to Multimodal Learning for Clinical Prediction
Medical University of Vienna（维也纳医科大学）；Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna（维也纳医科大学医学人工智能综合中心）；Christian Doppler Laboratory for Machine Learning Driven Precision Imaging（克里斯蒂安·多普勒机器学习驱动精准成像实验室）
Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design
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）；Department of Computer Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）
