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Daily Signal January 19, 2026 · 10 min read

Issue #25: Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models.

Protein Design Digest - 2026-01-19 - Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models.

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Signal of the Day

Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models.

Predicting cytochrome P450 (CYP450) ligand binding is critical in early-stage drug discovery as CYP450-mediated metabolism profoundly influences drug efficacy, safety, and adverse reaction risks. However, experimental determination of CYP450-ligand interactions remains resource- and time-intensive, underscoring the need for robust computational alternatives. While ligand-based methods are commonly employed, they often fail to fully account for structural intricacies governing protein-ligand interactions. To address this gap, we developed a hybrid machine learning framework integrating ligand descriptors, protein descriptors, and protein-ligand interaction descriptors that include molecular docking-derived parameters, rescoring function components from multiple algorithms, and structural interaction fingerprints (SIFt). Evaluated on CYP1A2 and CYP17A1 isoforms, our model demonstrated superior predictive accuracy in cross-validation compared with stand-alone molecular docking and ligand-based approaches. Furthermore, benchmarking against state-of-the-art tools (SwissADME and ADMETlab 3.0) revealed enhanced performance in binding prediction. This work establishes a versatile framework for advancing computational tools to prioritize CYP450 binding assessments during drug discovery.

Why this matters: Essential ground-truth data for validating next-gen foundation models like Boltz or Chai.

Also Worth Reading

Predicting the Mechanism of Action of Bawei Chufan Soup in Treating Teen Depression through Network Pharmacology, Molecular Docking and Molecular Dynamics Simulation.

Introduction The Bawei Chufan Soup (BWCFS) in Traditional Chinese Medicine (TCM) offers unique advantages in treating Teen Depression (TD). This study utilizes network pharmacology, molecular docking, and molecular dynamics simulations to predict the material basis and mechanism of action of the decoction. Methods The TCMSP, SwissADME, and SwissTargetPrediction databases were utilized to obtain the active ingredients and targets of the BWCFS. The GeneCards, OMIM, and Disgenet databases were used to identify disease targets, and the intersection of these sets was determined using the VENNY tool. The intersecting targets were imported into the String database for protein- protein interaction analysis and the screening of core targets. GO and KEGG enrichment analyses of the intersecting targets were conducted using the David database, and drugcomponent- target-pathway network diagrams were constructed using Cytoscape 3.10.0 software. The molecular docking models of the core components and key targets were generated using AutoDock Vina, and kinetic simulations were conducted using GROMACS 2020.3, paired with the best docking models. Results After screening, the study identified the core components of BWCFS as Baicalein, Kaempferol, Quercetin, Cerevisterol, and Cavidine, with the key targets for TD being AKT1, IL6, TNF, ESR1, and IL1B. GO enrichment analysis revealed that BWCFS may affect signal transduction in the treatment of TD, and is associated with cellular components such as the plasma membrane and dendrites, as well as the regulation of protein binding. KEGG analysis suggested that the intersecting genes are primarily enriched in the cyclic adenosine monophosphate (cAMP) signaling pathway. Molecular docking results indicated that AKT1 shows good binding affinity with Baicalein, Cavidine, Kaempferol, and Quercetin, while Cerevisterol exhibits strong binding with TNF. The molecular dynamics simulations were stable and reliable. During the protein-ligand complex simulation, the binding between the protein and ligand was stable, with van der Waals interactions as the primary force, while hydrogen bonds were present between both the protein and ligand. Discussion Though this study has several common limitations associated with network pharmacology, and no animal experiments have been conducted for verification, the study has successfully explored and validated the mechanism of action of BWCFS in treating TD using scientific computational methods. This study provides new perspectives and methods for the development and management of pharmacological treatments for TD, offering innovative insights into TCM approaches for its treatment. Conclusion Through network pharmacology, this study preliminarily predicted the material basis and mechanism of action of BWCFS in treating TD. Furthermore, the therapeutic effects of BWCFS on TD may be associated with neuroinflammation and structural and functional changes in neuronal dendrites. The cAMP-PKA-NF-κB and cAMP-PI3K-AKT-NF-κB pathways are proposed as potential therapeutic targets.

MetalloDock: Decoding Metalloprotein-Ligand Interactions via Physics-Aware Deep Learning for Metalloprotein Drug Discovery.

Accurate prediction of metalloprotein-ligand interactions is critical for metalloprotein-targeted drug discovery. Conventional docking tools and existing deep learning (DL) models fail to reliably capture metal-ligand interactions, hampering the discovery of potent metalloprotein inhibitors. Here, we propose MetalloDock, the first DL-based docking framework specially designed for metalloprotein targets. By innovatively integrating an autoregressive spatial decoding engine with a physics-constrained geometric generation paradigm, MetalloDock can precisely reconstruct metal coordination geometries and accurately capture metal-ligand interactions, which enhance both the accuracy of metalloprotein-ligand docking and binding affinity prediction. Extensive evaluations on our custom-built benchmark data set demonstrate that MetalloDock outperforms existing methods, including AlphaFold3, in docking success rate and virtual screening performance for metalloprotein targets. In real-world applications, MetalloDock successfully identified multiple novel hit compounds in a virtual screening campaign targeting the prostate-specific membrane antigen. Additionally, it enabled rational drug design for acidic polymerase endonuclease, leading to the discovery of potent inhibitors. These results highlight the broad applicability of MetalloDock in accelerating metalloprotein-targeted drug discovery and provide a standardized framework for future evaluation of metalloprotein-specific docking algorithms.

In silico screening of IMPPAT-derived phytochemicals targeting ERG6 and drug resistance-associated proteins in drug-resistant Candida albicans: virtual screening and molecular dynamics using alphafold models.

Pathogenic fungi, particularly Candida albicans, have been escalating clinical problems, notably because of antifungal resistance and symptomatological comorbidity with COVID-19. This research aimed to find phytochemical inhibitors of ergosterol production, specifically targeting ERG6 (C-24 sterol methyltransferase), utilizing chemicals from the IMPPAT database. A total of 14,965 phytochemicals were computationally evaluated against AlphaFold-predicted ERG6 utilizing AutoDock Vina. Fifteen compounds exhibiting robust binding affinities (- 8.2 to - 9.2 kcal/mol) were found, from which four candidates were chosen based on advantageous ADMET profiles. The docking scores for the top four compounds targeting ERG6-Daturataturin A (- 8.8 kcal/mol), Disogluside (- 8.6), Tataramide B (- 8.4), and Floribundasaponin A (- 8.4)-exceeded those of previously identified ERG6 inhibitors D28 (- 8.0), Tomatidine (- 7.9), and H55 (- 6.4). The selected leads were further docked against other proteins associated with drug resistance and cell proliferation, specifically ERG1, ERG11, CLB2, CDR1, and CDR2. Among these, only ERG1 exhibited significant interactions, with Disogluside (- 9.3 kcal/mol), Tataramide B (- 9.9), and Floribundasaponin A (- 9.3) surpassing the reference inhibitor terbinafine (- 8.7 kcal/mol), except for Daturataturin A, which showed a comparable score of - 8.6 kcal/mol. Nevertheless, owing to steric conflicts inside the ERG1 binding sites, molecular dynamics (MD) simulations were conducted exclusively for ERG6-ligand complexes over duration of 100 ns. The RMSD values demonstrated commendable structural stability: Daturataturin A (~ 0.39 nm), Disogluside (~ 0.38 nm), Tataramide B (~ 0.27 nm), and Floribundasaponin A (~ 0.40 nm). Principal Component Analysis (PCA) validated consistent and significant movements for Daturataturin A and Floribundasaponin A, whereas Disogluside and Tataramide B exhibited increased flexibility. MM/PBSA analysis indicated robust binding free energies for Daturataturin A (- 42.26 kcal/mol), Floribundasaponin A (- 37.48 kcal/mol), and Disogluside (- 29.58 kcal/mol), however Tataramide B exhibited a detrimental + 9.81 kcal/mol. These results endorse the promise of phytochemical-derived antifungals and necessitate more experimental verification. The online version contains supplementary material available at 10.1007/s40203-025-00480-9.

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