AIMC Topic: Proteins

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HPDAF: A practical tool for predicting drug-target binding affinity using multimodal features.

European journal of medicinal chemistry
Accurate prediction of drug-target binding affinity is crucial for efficient drug discovery and design, enabling researchers to better understand molecular interactions and accelerate the identification of promising drug candidates. Despite recent ad...

ProteinWeaver: A webtool to visualize ontology-annotated protein networks.

PloS one
Molecular interaction networks are a vital tool for studying biological systems. While many tools exist that visualize a protein or a pathway within a network, no tool provides the ability for a researcher to consider a protein's position in a networ...

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins.

Journal of chemical information and modeling
Lipids are essential metabolites that play critical roles in multiple cellular pathways. Like many primary metabolites, mutations that disrupt lipid synthesis can be lethal. Proteins involved in lipid synthesis, trafficking, and modification, are tar...

All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models.

Journal of chemical information and modeling
Proteochemometric models (PCMs) are used in computational drug discovery to employ both protein and ligand representations jointly for bioactivity prediction. While machine learning (ML) and deep learning (DL) have come to dominate PCMs, often servin...

Combining knowledge distillation and neural networks to predict protein secondary structure.

Scientific reports
The secondary structure of a protein serves as the foundation for constructing its three-dimensional (3D) structure, which in turn is critical for determining its function and role in biological processes. Therefore, accurately predicting secondary s...

Graph Learning-Based Scoring of RNA-Protein Complex Structures.

Journal of chemical theory and computation
Development of suitable scoring functions is essential for the prediction of RNA-protein complex structures. Conventional statistical potential-based scoring functions suffered from deficiencies in handling conformational flexibility. The recent appl...

Phenotypic Screening for Targeted Protein Degradation: Strategies, Challenges, and Emerging Opportunities.

Journal of medicinal chemistry
Phenotypic screening is undergoing a resurgence in the field of targeted protein degradation as a powerful complement to target-based approaches, which are often constrained by requirements for detailed structural and ligand-binding information. Phen...

PepBAN: A Deep Learning Framework with Bilinear Attention and Adversarial Learning for Peptide-Protein Interaction Prediction.

Journal of chemical information and modeling
Accurate prediction of the peptide-protein interaction (PepPI) is crucial for developing peptide-based therapeutics and vaccines. However, this computational task has traditionally faced significant challenges, such as the scarcity of structure data ...

BiVAE-CPI: An Interpretable Generative Model Using a Bilateral Variational Autoencoder for Compound-Protein Interaction Prediction.

Journal of chemical information and modeling
Predicting compound-protein interaction (CPI) plays a critical role in drug discovery and development, but traditional screening experiments consume much time and resources. Therefore, deep learning methods for CPI prediction are popular now. However...

Protein functional site annotation using local structure embeddings.

Proceedings of the National Academy of Sciences of the United States of America
The rapid expansion of protein sequence and structure databases has resulted in a significant number of proteins with ambiguous or unknown function. While advances in machine learning techniques hold great potential to fill this annotation gap, curre...