Drug–Target Interaction Prediction via Dual-Interaction Fusion

Li et al., Molecules, doi:10.3390/molecules31030498, Jan 2026
Ivermectin for COVID-19
4th treatment shown to reduce risk in August 2020, now with p < 0.00000000001 from 106 studies, recognized in 24 countries.
Lower risk for mortality, ventilation, ICU, hospitalization, recovery, cases, and viral clearance.
No treatment is 100% effective. Protocols combine treatments.
6,400+ studies for 210+ treatments. c19early.org
Meta-analysis
In silico study showing that the GADFDTI computational model successfully predicts drug-target interactions for SARS-CoV-2 proteins, achieving high prediction accuracy with AUC values of 0.986 and 0.996 on benchmark datasets. The model assigned high interaction probabilities to clinically validated antivirals including baricitinib, remdesivir, ritonavir, and lopinavir for 3CLpro binding, while predicting strong interactions for ivermectin, sofosbuvir, remdesivir, daclatasvir, lopinavir, and ritonavir with RdRp.
75 preclinical studies support the efficacy of ivermectin for COVID-19:
35 in silico studies1-35
26 in vitro studies3,36-60
14 in vivo animal studies47,53,60-71
Ivermectin, better known for antiparasitic activity, is a broad spectrum antiviral with activity against many viruses including H7N772, Dengue38,73,74, HIV-174, Simian virus 4075, Zika38,76,77, West Nile77, Yellow Fever78,79, Japanese encephalitis78, Chikungunya79, Semliki Forest virus79, Human papillomavirus58, Epstein-Barr58, BK Polyomavirus80, and Sindbis virus79.
Ivermectin inhibits importin-α/β-dependent nuclear import of viral proteins72,74,75,81, shows spike-ACE2 disruption at 1nM with microfluidic diffusional sizing39, binds to glycan sites on the SARS-CoV-2 spike protein preventing interaction with blood and epithelial cells and inhibiting hemagglutination42,82, shows dose-dependent inhibition of wildtype and omicron variants37, exhibits dose-dependent inhibition of lung injury62,67, may inhibit SARS-CoV-2 via IMPase inhibition38, may inhibit SARS-CoV-2 induced formation of fibrin clots resistant to degradation10, inhibits SARS-CoV-2 3CLpro55, may inhibit SARS-CoV-2 RdRp activity1,29, may minimize viral myocarditis by inhibiting NF-κB/p65-mediated inflammation in macrophages61, may be beneficial for COVID-19 ARDS by blocking GSDMD and NET formation83, may interfere with SARS-CoV-2's immune evasion via ORF8 binding5, may inhibit SARS-CoV-2 by disrupting CD147 interaction84-87, may inhibit SARS-CoV-2 attachment to lipid rafts via spike NTD binding3, shows protection against inflammation, cytokine storm, and mortality in an LPS mouse model sharing key pathological features of severe COVID-1960,88, may be beneficial in severe COVID-19 by binding IGF1 to inhibit the promotion of inflammation, fibrosis, and cell proliferation that leads to lung damage9, may minimize SARS-CoV-2 induced cardiac damage41,49, may counter immune evasion by inhibiting NSP15-TBK1/KPNA1 interaction and restoring IRF3 activation89, may disrupt SARS-CoV-2 N and ORF6 protein nuclear transport and their suppression of host interferon responses2, reduces TAZ/YAP nuclear import, relieving SARS-CoV-2-driven suppression of IRF3 and NF-κB antiviral pathways36, increases Bifidobacteria which play a key role in the immune system90, has immunomodulatory52 and anti-inflammatory71,91 properties, and has an extensive and very positive safety profile92.
Important missing comments or errors? Let us know.
Study covers ivermectin and remdesivir.
Li et al., 31 Jan 2026, China, peer-reviewed, 4 authors. Contact: yunizeng@zstu.edu.cn (corresponding author), 2023337621278@mails.zstu.edu.cn, zepengli56@gmail.com, weibo@zstu.edu.cn.
In silico studies are an important part of preclinical research, however results may be very different in vivo.
Drug–Target Interaction Prediction via Dual-Interaction Fusion
Xingyang Li, Zepeng Li, Bo Wei, Yuni Zeng
Molecules, doi:10.3390/molecules31030498
Accurate prediction of drug-target interaction (DTI) is crucial for modern drug discovery. However, experimental assays are costly, and many existing computational models still face challenges in capturing multi-scale features, fusing cross-modal information, and modeling fine-grained drug-protein interactions. To address these challenges, We propose Gated-Attention Dual-Fusion Drug-Target Interaction (GADFDTI), whose core contribution is a fusion module that constructs an explicit atom-residue similarity field, refines it with a lightweight 2D neighborhood operator, and performs gated bidirectional aggregation to obtain interaction-aware representations. To provide strong and width-aligned unimodal inputs to this fusion module, we integrate a compact multi-scale dense GCN for drug graphs and a masked multi-scale self-attention protein encoder augmented by a narrow 1D-CNN branch for local motif aggregation. Experiments on two benchmarks, Human and C. elegans, show that GADFDTI consistently outperforms several recently proposed DTI models, achieving AUC values of 0.986 and 0.996, respectively, with corresponding gains in precision and recall. A SARS-CoV-2 case study further demonstrates that GADFDTI can reliably prioritize clinically supported antiviral agents while suppressing inactive compounds, indicating its potential as an efficient in silico prescreening tool for lead-target discovery.
Supplementary Materials: The following supporting information can be downloaded at: https://www. mdpi.com/article/10.3390/molecules31030498/s1 , Supplementary Table S1 : Paired t-test results across 10 matched random seeds (two-sided, n = 10, α = 0.05) comparing GADFDTI with baseline methods. AUC is reported as mean ± standard deviation across seeds. Mean difference = (GADFDTI-baseline). p-values reported as "p < 1 × 10 -4 " when printed as 0.0000 in logs; Supplementary Table S2 . Slidingwindow aggregation for long proteins (inference only). Proteins longer than 1200 residues are evaluated on the >1200 subset using two inference strategies: (i) truncation to the first 1200 residues; (ii) sliding-window inference with window length = 1200, stride = 300, and max pooling over window-level scores. Values are reported as mean ± standard deviation over 10 random seeds. Author Contributions: Conceptualization, Z.L. and Y.Z.; methodology, Z.L., X.L. and B.W.; software, Z.L. and X.L.; validation, Z.L. and X.L.; formal analysis, Z.L. and X.L.; investigation, Z.L. and X.L.; resources, Y.Z. and B.W.; data curation, Z.L. and X.L.; writing-original draft preparation, Z.L.; writing-review and editing, Y.Z., X.L. and B.W.; visualization, Z.L. and X.L.; supervision, Y.Z. and B.W.; project administration, Y.Z. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest: The authors declare no conflicts of interest. Abbreviations..
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To provide strong and width-aligned unimodal inputs to this fusion module, we integrate a compact multi-scale dense GCN for drug graphs and a masked multi-scale self-attention protein encoder augmented by a narrow 1D-CNN branch for local motif aggregation. Experiments on two benchmarks, Human and C. elegans, show that GADFDTI consistently outperforms several recently proposed DTI models, achieving AUC values of 0.986 and 0.996, respectively, with corresponding gains in precision and recall. 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Please send us corrections, updates, or comments. c19early involves the extraction of 200,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. Treatments and other interventions are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. IMA and WCH provide treatment protocols.
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