MVCL-DTI: Predicting Drug-Target Interactions Using a Multiview Contrastive Learning Model on a Heterogeneous Graph.

Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizing information from varied biological subnetworks. It integrates neighbor view, meta-path view, and diffusion view to capture semantic features and employs an attention-based contrastive learning approach, along with a multiview attention-weighted fusion module, to effectively integrate and adaptively weight the information from the different views.

DSE-HNGCN: Predicting the frequencies of drug-side effects based on heterogeneous networks with mining interactions between drugs and side effects.

Evaluating the frequencies of drug-side effects is crucial in drug development and risk-benefit analysis. While existing deep learning methods show promise, they have yet to explore using heterogeneous networks to simultaneously model the various relationship between drugs and side effects, highlighting areas for potential enhancement. In this study, we propose DSE-HNGCN, a novel method that leverages heterogeneous networks to simultaneously model the various relationships between drugs and side effects.

DTA-GTOmega: Enhancing Drug-Target Binding Affinity Prediction with Graph Transformers Using OmegaFold Protein Structures.

Understanding drug-protein interactions is crucial for elucidating drug mechanisms and optimizing drug development. However, existing methods have limitations in representing the three-dimensional structure of targets and capturing the complex relationships between drugs and targets. This study proposes a new method, DTA-GTOmega, for predicting drug-target binding affinity.

RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.

Motivation: Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two.

DGCddG: Deep Graph Convolution for Predicting Protein-Protein Binding Affinity Changes Upon Mutations.

Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure.

Simultaneous Prediction of Interaction Sites on the Protein and Peptide Sides of Complexes through Multilayer Graph Convolutional Networks.

Identifying the binding residues of protein-peptide complexes is essential for understanding protein function mechanisms and exploring drug discovery. Recently, many computational methods have been developed to predict the interaction sites of either protein or peptide. However, to our knowledge, no prediction method can simultaneously identify the interaction sites on both the protein and peptide sides.

DeepNup: Prediction of Nucleosome Positioning from DNA Sequences Using Deep Neural Network.

Nucleosome positioning is involved in diverse cellular biological processes by regulating the accessibility of DNA sequences to DNA-binding proteins and plays a vital role. Previous studies have manifested that the intrinsic preference of nucleosomes for DNA sequences may play a dominant role in nucleosome positioning. As a consequence, it is nontrivial to develop computational methods only based on DNA sequence information to accurately identify nucleosome positioning, and thus intend to verify the contribution of DNA sequences responsible for nucleosome positioning.

Identifying modifications on DNA-bound histones with joint deep learning of multiple binding sites in DNA sequence.

Motivation: Histone modifications are epigenetic markers that impact gene expression by altering the chromatin structure or recruiting histone modifiers. Their accurate identification is key to unraveling the mechanisms by which they regulate gene expression. However, the solutions for this task can be improved by exploiting multiple relationships from dataset and exploring designs of learning models, for example jointly learning technology.

SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model.

A large number of inorganic and organic compounds are able to bind DNA and form complexes, among which drug-related molecules are important. Chromatin accessibility changes not only directly affect drug-DNA interactions, but they can promote or inhibit the expression of the critical genes associated with drug resistance by affecting the DNA binding capacity of TFs and transcriptional regulators. However, the biological experimental techniques for measuring it are expensive and time-consuming.

ctPISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation.

Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods.