ArticleInternational journal of molecular sciences2021
SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Review
- Prediction of bacterial protein-compound interactions with only positive samples.Bioinformatics (Oxford, England) · 2026Article
- NCIVISION: A Siamese Neural Network for Molecular Similarity Prediction MEP and RDG Images.Molecules (Basel, Switzerland) · 2025Article
- Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks.Journal of chemical information and modeling · 2025Article
- Recent computational advances in the identification of cryptic binding sites for drug discovery.Bioinformatics advances · 2025Review
- Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review.Computational and structural biotechnology journal · 2024Review
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- A comparison of embedding aggregation strategies in drug-target interaction prediction.BMC bioinformatics · 2024Article
- Diffusion models in bioinformatics and computational biology.Nature reviews bioengineering · 2024Article
- GeNNius: an ultrafast drug-target interaction inference method based on graph neural networks.Bioinformatics (Oxford, England) · 2024Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- A review onExploration of targeted anti-tumor therapy · 2023Review
- Allosteric control of ACE2 peptidase domain dynamics.Organic & biomolecular chemistry · 2022Article
- Artificial Intelligence Technologies for COVID-19 De Novo Drug Design.International journal of molecular sciences · 2022Review
- Screening S protein - ACE2 blockers from natural products: Strategies and advances in the discovery of potential inhibitors of COVID-19.European journal of medicinal chemistry · 2021Review
- The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes.Molecules (Basel, Switzerland) · 2021Review
- MCN-CPI: Multiscale Convolutional Network for Compound-Protein Interaction Prediction.Biomolecules · 2021Article
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Authors and funding
9 authors.
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Abstract
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.