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HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions [0.03%] 基于图神经网络加速预测不同PH条件下的水解自由能
Rishabh D Guha,Santiago Vargas,Evan Walter Clark Spotte-Smith et al. Rishabh D Guha et al.
Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water's remarkable versatility as a solvent. However, accurately ...
Drug-Target Affinity Prediction Based on Topological Enhanced Graph Neural Networks [0.03%] 基于拓扑增强图神经网络的药物靶点亲和力预测
Hengliang Guo,Congxiang Zhang,Jiandong Shang et al. Hengliang Guo et al.
Graph neural networks (GNNs) have achieved remarkable success in drug-target affinity (DTA) analysis, reducing the cost of drug development. Unlike traditional one-dimensional (1D) sequence-based methods, GNNs leverage graph structures to c...
DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials [0.03%] DeePMD-GNN: 用于外部图神经网络势能的DeePMD-kit插件
Jinzhe Zeng,Timothy J Giese,Duo Zhang et al. Jinzhe Zeng et al.
Machine learning potentials (MLPs) have revolutionized molecular simulation by providing efficient and accurate models for predicting atomic interactions. MLPs continue to advance and have had profound impact in applications that include dr...
CL-GNN: Contrastive Learning and Graph Neural Network for Protein-Ligand Binding Affinity Prediction [0.03%] 基于对比学习和图神经网络的蛋白质-配体结合亲和力预测方法
Yunjiang Zhang,Chenyu Huang,Yaxin Wang et al. Yunjiang Zhang et al.
In the realm of drug discovery and design, the accurate prediction of protein-ligand binding affinity is of paramount importance as it underpins the functional interactions within biological systems. This study introduces a novel self-super...
Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks [0.03%] 基于图神经网络的数据约束条件下改善活化能预测
Han-Chung Chang,Ming-Hsuan Tsai,Yi-Pei Li Han-Chung Chang
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale s...
Graph-Based Deep Learning Models for Thermodynamic Property Prediction: The Interplay between Target Definition, Data Distribution, Featurization, and Model Architecture [0.03%] 基于图的深度学习模型预测热力学性质：目标定义、数据分布、特征表示和模型架构之间的相互作用
Bowen Deng,Thijs Stuyver Bowen Deng
In this contribution, we examine the interplay between target definition, data distribution, featurization approaches, and model architectures on graph-based deep learning models for thermodynamic property prediction. Through consideration ...
ProAffinity-GNN: A Novel Approach to Structure-Based Protein-Protein Binding Affinity Prediction via a Curated Data Set and Graph Neural Networks [0.03%] 基于结构的蛋白质相互作用亲和力预测的新型方法ProAffinity-GNN
Zhiyuan Zhou,Yueming Yin,Hao Han et al. Zhiyuan Zhou et al.
Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinitie...
ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction [0.03%] ChemXTree：一种增强的图神经网络-决策树框架用于ADMET预测
Yuzhi Xu,Xinxin Liu,Wei Xia et al. Yuzhi Xu et al.
The rapid progression of machine learning, especially deep learning (DL), has catalyzed a new era in drug discovery, introducing innovative approaches for predicting molecular properties. Despite the many methods available for feature repre...
GGAS2SN: Gated Graph and SmilesToSeq Network for Solubility Prediction [0.03%] 基于门控图神经网络和SmilesToSeq模型的溶解度预测方法
Waqar Ahmad,Kil To Chong,Hilal Tayara Waqar Ahmad
Aqueous solubility is a critical physicochemical property of drug discovery. Solubility is a key issue in pharmaceutical development because it can limit a drug's absorption capacity. Accurate solubility prediction is crucial for pharmacolo...
Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks [0.03%] 利用可解释图神经网络识别植物杀菌剂配方中的协同成分
Oliver Snow,Amirreza Kazemi,Forum Bhanshali et al. Oliver Snow et al.
Botanical formulations are promising candidates for developing new biopesticides that can protect crops from pests and diseases while reducing harm to the environment. These biopesticides can be combined with permeation enhancer compounds t...