Paper · 39221997

DTI-LM: language model powered drug-target interaction prediction.

Ahmed KT and 2 others. Bioinformatics, 2024. Cited by 30 papers, by iCite.

11 sourcespapers-2026-11-12-0.1.0

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ConductScience Library. DTI-LM: language model powered drug-target interaction prediction. Papers, record 39221997. Release papers-2026-11-12-0.1.0, 12 Nov 2026. https://conductscience.org/library/papers/39221997

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Paper

PubMed's record of the paper, with iCite's citation figures. No abstract is shown.

Title: DTI-LM: language model powered drug-target interaction prediction.; PubMed
Journal: Bioinformatics; PubMed
Year: 2024; PubMed
Publication types: Journal Article, Research Support, Non-U.S. Gov't; PubMed
PubMed: https://pubmed.ncbi.nlm.nih.gov/39221997/; PubMed
DOI: https://doi.org/10.1093/bioinformatics/btae533; PubMed
PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC11520403/; PubMed
The journal: https://conductscience.org/library/journal-index/9808944; PubMed
Citations, by iCite: 30; iCite
Relative citation ratio, by iCite: 5.12, provisional; iCite
Revised in PubMed: 2026-09-11; PubMed

Authors3

The authors in the order PubMed's record lists them, with the ORCID iDs it states.

Order	Author	Forename	ORCID iD
1	Ahmed KT	Khandakar Tanvir
2	Ansari MI	Md Istiaq
3	Zhang W	Wei	https://orcid.org/0000-0003-3605-9373

Source PubMed

Topics6

NLM's MeSH indexing of the paper, its major topics first.

MeSH term	Major
Proteins	Major topic
Computational Biology	Major topic
Drug Discovery
Pharmaceutical Preparations
Algorithms
Software

Source PubMed

Grants1

Each grant PubMed's record writes, matched to the NIH project NIH's grant and paper links name for this paper, and each of those links the record does not write.

Grant, as the record writes it	Agency	Country	NIH project	Stated by
NSF-III2246796	National Science Foundation			PubMed

Source PubMed

Trials0

The trials whose ClinicalTrials.gov record lists the paper, and the trial numbers the paper's PubMed record names.

None in this release.

Comments and corrections0

PubMed's comments and corrections, NLM's own indexing: retractions first, then expressions of concern, errata and updates, and comments last.

None in this release.

References35

The papers the reference list in PubMed's record names, those in our copy of PubMed, in the list's order.

Order	PMID	Title	Journal	Year	In Papers	Source
1	35,097,268	TransDTI: Transformer-Based Language Models for Estimating DTIs and Building a Drug Recommendation Workflow.	ACS Omega	2022	https://conductscience.org/library/papers/35097268	PubMed
2	38,221,904	Predicting drug-target binding affinity with cross-scale graph contrastive learning.	Brief Bioinform	2023	https://conductscience.org/library/papers/38221904	PubMed
3	24,203,711	DrugBank 4.0: shedding new light on drug metabolism.	Nucleic Acids Res	2014	https://conductscience.org/library/papers/24203711	PubMed
4	34,759,309	Clinical development times for innovative drugs.	Nat Rev Drug Discov	2022	https://conductscience.org/library/papers/34759309	PubMed
5	35,652,721	Effective drug-target interaction prediction with mutual interaction neural network.	Bioinformatics	2022	https://conductscience.org/library/papers/35652721	PubMed
6	18,586,719	Prediction of drug-target interaction networks from the integration of chemical and genomic spaces.	Bioinformatics	2008	https://conductscience.org/library/papers/18586719	PubMed
7	28,924,171	A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.	Nat Commun	2017	https://conductscience.org/library/papers/28924171	PubMed
8	17,145,705	BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities.	Nucleic Acids Res	2007	https://conductscience.org/library/papers/17145705	PubMed
9	28,368,587	Protein-Ligand Scoring with Convolutional Neural Networks.	J Chem Inf Model	2017	https://conductscience.org/library/papers/28368587	PubMed
10	32,428,219	TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments.	Bioinformatics	2020	https://conductscience.org/library/papers/32428219	PubMed

1 to 10 of 35

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Cited by23

The papers in our copy of PubMed whose reference list names this one, newest first. iCite, which reads more sources, counts 30 citations.

PMID	Title	Journal	Year	In Papers	Source
42,850,682	Mechanism-Informed Language Modeling and Oxygenated 3D Screening Identify Berberine-Enzalutamide Synergy in Prostate Cancer Models.	Adv Sci (Weinh)	2026	https://conductscience.org/library/papers/42850682	PubMed
42,828,305	Multiscale cross modal fusion and cascaded signal propagation for drug target interaction prediction.	iScience	2026	https://conductscience.org/library/papers/42828305	PubMed
42,630,545	Technical review of artificial intelligence in TCR-T therapy.	J Natl Cancer Cent	2026	https://conductscience.org/library/papers/42630545	PubMed
42,519,174	ColdstartMHDTI: integrating biomolecular pretraining and attention-based heterogeneous graph learning for drug-target interaction prediction.	Front Chem	2026	https://conductscience.org/library/papers/42519174	PubMed
42,441,587	T-pGNN4DTI: Towards better drug-target interactions prediction using Global Self-attentive Pooled Graph Convolutional Networks and protein pre-training Models.	PLoS One	2026	https://conductscience.org/library/papers/42441587	PubMed
42,428,845	AD-LSF: A Multimodal Framework for Drug-Target Interaction Prediction Based on Asymmetric Dynamic Gated Modulation and Adaptive Latent Signal Fusion.	ACS Omega	2026	https://conductscience.org/library/papers/42428845	PubMed
42,330,084	Beyond the canonical: The role of post-transcriptional regulation in drug-target interaction prediction.	PLoS Comput Biol	2026	https://conductscience.org/library/papers/42330084	PubMed
42,212,979	How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.	Adv Sci (Weinh)	2026	https://conductscience.org/library/papers/42212979	PubMed
42,157,243	KmalPred: a deep learning framework for lysine malonylation site prediction using protein language model representations.	BMC Biol	2026	https://conductscience.org/library/papers/42157243	PubMed
42,021,142	Protein and ligand novelty in drug-target interaction prediction: a dual-encoder fusion strategy for more interpretable and generalizable modeling.	BMC Bioinformatics	2026	https://conductscience.org/library/papers/42021142	PubMed

1 to 10 of 23

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Genes0

The genes the Gene search release ties to the paper: Swiss-Prot's references first, then PubTator's text mining.

None in this release.

Drugs0

The drugs whose trials, in the Pipelines release, list the paper.

None in this release.

Data0

The data bank entries PubMed's record names. Trial numbers are under Trials.

None in this release.

Data dates

The date of each source this release read.

PubMed: 2026-11-06; PubMed
iCite: 2026-11-05; iCite
PMC's ID file: 2026-11-05; PMC's ID file
NIH RePORTER's grant and paper links: 2026-11-01; NIH RePORTER's grant and paper links
Trials: 2026-11-12
Gene search: 2026-11-11
Pipelines: 2026-09-18

Sources 11

ChEMBL, through Open TargetsCC BY-SA 3.0
ClinicalTrials.govNo license stated
HGNCCC0
iCiteCC BY 4.0
NCBI GenePublic domain (US government)
NIH RePORTER's grant and paper linksPublic domain
Open TargetsCC0 1.0
PMC's ID filePublic domain (US government)
PubMedNLM's terms
PubTator 3Public domain
UniProtCC BY 4.0

Each source's terms

ChEMBL, through Open TargetsCC BY-SA 3.0, for the parts from ChEMBL. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
ClinicalTrials.govnone stated
HGNCCC0: https://www.genenames.org/about/license/
iCiteCC BY 4.0
NCBI GeneNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
NIH RePORTER's grant and paper linksPublic domain (NIH RePORTER)
Open TargetsCC0 1.0. Open Targets asks that users cite its latest paper: Buniello et al. 2025, Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery, Nucleic Acids Research 53(D1):D1467, PMID 39657122. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
PMC's ID fileNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
PubMedNLM's terms
PubTator 3Public domain
UniProtCC BY 4.0. UniProt's licence file: "All databases and documents in the UniProt FTP directory may be copied and redistributed freely, without advance permission, provided that this copyright statement is reproduced with each copy." UniProt asks that users cite its latest paper: The UniProt Consortium, UniProt: the Universal Protein Knowledgebase in 2025, Nucleic Acids Research 53:D609-D617, https://doi.org/10.1093/nar/gkae1010. Read on 9 Nov 2026 at https://www.uniprot.org/help/license

ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.

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