ArticleMolecules (Basel, Switzerland)2019
Drug-Target Interaction Prediction Based on Drug Fingerprint Information and Protein Sequence.
Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen health care settings.GigaScience · 2026Article
- TriDTI: tri-modal representation learning with cross-modal alignment for drug-target interaction prediction.Briefings in bioinformatics · 2026Article
- AΙ-Driven Drug Repurposing: Applications and Challenges.Medicines (Basel, Switzerland) · 2025Review
- Advances and challenges in drug repurposing in precision therapeutics of colorectal cancer.World journal of gastrointestinal oncology · 2025Review
- HPOseq: a deep ensemble model for predicting the protein-phenotype relationships based on protein sequences.BMC bioinformatics · 2025Article
- MFCADTI: improving drug-target interaction prediction by integrating multiple feature through cross attention mechanism.BMC bioinformatics · 2025Article
- Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics.bioRxiv : the preprint server for biology · 2024Article
- NFSA-DTI: A Novel Drug-Target Interaction Prediction Model Using Neural Fingerprint and Self-Attention Mechanism.International journal of molecular sciences · 2024Article
- Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview.Current drug discovery technologies · 2024Review
- Factor-specific generative pattern from large-scale drug-induced gene expression profile.Scientific reports · 2023Article
- Drug-target interaction prediction based on protein features, using wrapper feature selection.Scientific reports · 2023Article
- Drug-target interaction prediction via multiple classification strategies.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The identification of drug-target interactions (DTIs) is a critical step in drug development. Experimental methods that are based on clinical trials to discover DTIs are time-consuming, expensive, and challenging. Therefore, as complementary to it, developing new computational methods for predicting novel DTI is of great significance with regards to saving cost and shortening the development period. In this paper, we present a novel computational model for predicting DTIs, which uses the sequence information of proteins and a rotation forest classifier. Specifically, all of the target protein sequences are first converted to a position-specific scoring matrix (PSSM) to retain evolutionary information. We then use local phase quantization (LPQ) descriptors to extract evolutionary information in the PSSM. On the other hand, substructure fingerprint information is utilized to extract the features of the drug. We finally combine the features of drugs and protein together to represent features of each drug-target pair and use a rotation forest classifier to calculate the scores of interaction possibility, for a global DTI prediction. The experimental results indicate that the proposed model is effective, achieving average accuracies of 89.15%, 86.01%, 82.20%, and 71.67% on four datasets (i.e., enzyme, ion channel, G protein-coupled receptors (GPCR), and nuclear receptor), respectively. In addition, we compared the prediction performance of the rotation forest classifier with another popular classifier, support vector machine, on the same dataset. Several types of methods previously proposed are also implemented on the same datasets for performance comparison. The comparison results demonstrate the superiority of the proposed method to the others. We anticipate that the proposed method can be used as an effective tool for predicting drug-target interactions on a large scale, given the information of protein sequences and drug fingerprints.
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Registered trials
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.