{"title":"Understanding the molecular insights of marketed liposomal drugs using molecular dynamics simulations of reduced scale coarse-grained models.","authors":"Ruifeng Wang, Jieying Zang, Hao Zhong, Yunsen Zhang, Defang Ouyang","doi":"10.1038/s44386-025-00014-5","DOIUrl":"10.1038/s44386-025-00014-5","url":null,"abstract":"<p><p>The complexity of the internal structure of liposome remains insufficiently understood, posing challenges to the rational design and development of liposomal formulations. In this study, we employed coarse-grained molecular dynamics simulation to investigate the structural details of the commercially available nine liposomal formulations, with five passive loading systems (Arikayce Kit®, Visudyne®, Ambisome®, Mepact®, and Exparel®) and four active loading systems (Doxil®, Myocet®, Onivyde®, and Vyxeos®). In five passive loading liposome systems, protonation of drug molecules significantly reduces their binding to phospholipid membranes and even drastically alters the morphology and vesicle size in multivesicular liposomes. However, the orientation of drug molecules in lipid membranes is independent of protonation, with hydrophobic part inward and hydrophilic part outward. In four active loading systems, the presence of ions within the liposome cavity promotes the self-aggregation of drugs, thereby enhancing drug retention and release profiles. This study provided valuable molecular insights for marketed liposomes formulation.</p>","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sumaiya Iqbal, Wei Jiang, Eric Hansen, Tonia Aristotelous, Shuang Liu, Andrew Reidenbach, Cerise Raffier, Alison Leed, Chengkuan Chen, Lawrence Chung, Eric Sigel, Alex Burgin, Sandy Gould, Holly H Soutter
{"title":"Evaluation of DNA encoded library and machine learning model combinations for hit discovery.","authors":"Sumaiya Iqbal, Wei Jiang, Eric Hansen, Tonia Aristotelous, Shuang Liu, Andrew Reidenbach, Cerise Raffier, Alison Leed, Chengkuan Chen, Lawrence Chung, Eric Sigel, Alex Burgin, Sandy Gould, Holly H Soutter","doi":"10.1038/s44386-025-00007-4","DOIUrl":"10.1038/s44386-025-00007-4","url":null,"abstract":"<p><p>DNA-Encoded Library (DEL) technology allows the screening of millions to billions of compounds in a pooled fashion, which is faster and cheaper than traditional approaches. The massive amounts of DEL binder and not-binder data enable Machine Learning (ML) model development and virtual screening of readily accessible, drug-like libraries in an ultra-high-throughput fashion. Here, we report a comparative assessment of DEL + ML pipeline for hit discovery using three DELs and five ML models (fifteen DEL + ML combinations). Each ML model was used to identify orthosteric binders of two therapeutic targets, Casein kinase 1α/δ (CK1α/δ). Overall, 10% and 94% of the predicted binders and not-binders were confirmed in biophysical assays, including two nanomolar binders (187 and 69.6 nM). Our study provides insights into the DEL + ML paradigm for hit discovery: the importance of chemical diversity in training data and ML model generalizability over accuracy. We publicly shared our results for further use and similar developments.</p>","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"FeatureDock for protein-ligand docking guided by physicochemical feature-based local environment learning using transformer.","authors":"Mingyi Xue, Bojun Liu, Siqin Cao, Xuhui Huang","doi":"10.1038/s44386-025-00005-6","DOIUrl":"10.1038/s44386-025-00005-6","url":null,"abstract":"<p><p>Molecular docking, the task of predicting the binding structures between a protein and a small molecule ligand, plays a significant role in structural-based drug discovery. In recent years, numerous deep learning-based methods for molecular docking have emerged. State-of-the-art approaches such as DiffDock formulate the docking problem using diffusion generative models, exhibiting superior performance than traditional docking algorithms. However, despite the strong performance of these deep learning-based docking methods in predicting binding poses, they often lack a well-defined scoring function. This limitation poses challenges in effectively distinguishing between the strong and weak inhibitors during virtual screening. To address this limitation, we introduce FeatureDock, a transformer-based deep learning framework, which can leverage chemical features from protein local environments to accurately predict the protein-ligand binding poses as well as achieve a strong scoring power for virtual screening. We demonstrate the robustness of FeatureDock on Cyclin-Dependent Kinase 2 and Angiotensin-converting enzyme compound datasets.</p>","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Riikka Huusari, Tianduanyi Wang, Sandor Szedmak, Tero Aittokallio, Juho Rousu
{"title":"Predicting drug combination response surfaces.","authors":"Riikka Huusari, Tianduanyi Wang, Sandor Szedmak, Tero Aittokallio, Juho Rousu","doi":"10.1038/s44386-024-00004-z","DOIUrl":"10.1038/s44386-024-00004-z","url":null,"abstract":"<p><p>Prediction of drug combination responses is a research question of growing importance for cancer and other complex diseases. Current machine learning approaches generally consider predicting either drug combination synergy summaries or single combination dose-response values, which fail to appropriately model the continuous nature of the underlying dose-response combination surface and can lead to inconsistencies when a synergy score or a dose-response matrix is reconstructed from separate predictions. We propose a novel prediction method, comboKR, that directly predicts the continuous drug combination response surface for a drug combination. The method is based on a powerful input-output kernel regression technique and functional modelling of the response surface. ComboKR belongs to the family of functional output regression methods, where the prediction target is a function, in our case, a non-linear parametric surface. Our method thus avoids predicting discretized forms of the target as scalars, vectors or matrices, and therefore provides better interpolation and extrapolation along the surfaces. As an important part of our approach, we develop a novel normalisation between response surfaces that standardises the heterogeneous experimental designs used to measure the dose-responses, and thus allows training the method with data measured in different laboratories. Our experiments on two predictive scenarios and using two combination datasets highlight the suitability of the proposed approach especially in the traditionally challenging setting of predicting combination responses for new drugs not available in the training data.</p>","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Augmented BindingNet dataset for enhanced ligand binding pose predictions using deep learning.","authors":"Hui Zhu, Xuelian Li, Baoquan Chen, Niu Huang","doi":"10.1038/s44386-024-00003-0","DOIUrl":"10.1038/s44386-024-00003-0","url":null,"abstract":"<p><p>High-quality data on protein-ligand complex structures and binding affinities are crucial for structure-based drug design. Existing datasets often lack diversity and quantity, limiting the comprehensive understanding of protein-ligand interactions. Here, we present BindingNet v2, an expanded dataset comprising 689,796 modeled protein-ligand binding complexes across 1794 protein targets. Constructed using an enhanced template-based modeling workflow from BindingNet v1, it incorporates pharmacophore and molecular shape similarities. BindingNet v2's effectiveness in binding pose generation was evaluated, showing an improved generalization ability of Uni-Mol model for novel ligands. The success rate on the PoseBusters dataset increased from 38.55% with the PDBbind dataset alone to 64.25% with augmenting BindingNet v2. Coupled with physics-based refinement, the success rate rose to 74.07%, passing PoseBusters validity checks. These results highlight the value of larger, diverse datasets in enhancing the accuracy and reliability of deep learning models for binding pose prediction.</p>","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
NPJ drug discoveryPub Date : 2025-01-01Epub Date: 2025-03-04DOI: 10.1038/s44386-025-00006-5
Karla P Godinez-Macias, Daisy Chen, J Lincoln Wallis, Miles G Siegel, Anna Adam, Selina Bopp, Krypton Carolino, Lauren B Coulson, Greg Durst, Vandana Thathy, Lisl Esherick, Madeline A Farringer, Erika L Flannery, Barbara Forte, Tiqing Liu, Luma Godoy Magalhaes, Anil K Gupta, Eva S Istvan, Tiantian Jiang, Krittikorn Kumpornsin, Karen Lobb, Kyle J McLean, Igor M R Moura, John Okombo, N Connor Payne, Andrew Plater, Srinivasa P S Rao, Jair L Siqueira-Neto, Bente A Somsen, Robert L Summers, Rumin Zhang, Michael K Gilson, Francisco-Javier Gamo, Brice Campo, Beatriz Baragaña, James Duffy, Ian H Gilbert, Amanda K Lukens, Koen J Dechering, Jacquin C Niles, Case W McNamara, Xiu Cheng, Lyn-Marie Birkholtz, Alfred W Bronkhorst, David A Fidock, Dyann F Wirth, Daniel E Goldberg, Marcus C S Lee, Elizabeth A Winzeler
{"title":"Revisiting the <i>Plasmodium falciparum</i> druggable genome using predicted structures and data mining.","authors":"Karla P Godinez-Macias, Daisy Chen, J Lincoln Wallis, Miles G Siegel, Anna Adam, Selina Bopp, Krypton Carolino, Lauren B Coulson, Greg Durst, Vandana Thathy, Lisl Esherick, Madeline A Farringer, Erika L Flannery, Barbara Forte, Tiqing Liu, Luma Godoy Magalhaes, Anil K Gupta, Eva S Istvan, Tiantian Jiang, Krittikorn Kumpornsin, Karen Lobb, Kyle J McLean, Igor M R Moura, John Okombo, N Connor Payne, Andrew Plater, Srinivasa P S Rao, Jair L Siqueira-Neto, Bente A Somsen, Robert L Summers, Rumin Zhang, Michael K Gilson, Francisco-Javier Gamo, Brice Campo, Beatriz Baragaña, James Duffy, Ian H Gilbert, Amanda K Lukens, Koen J Dechering, Jacquin C Niles, Case W McNamara, Xiu Cheng, Lyn-Marie Birkholtz, Alfred W Bronkhorst, David A Fidock, Dyann F Wirth, Daniel E Goldberg, Marcus C S Lee, Elizabeth A Winzeler","doi":"10.1038/s44386-025-00006-5","DOIUrl":"10.1038/s44386-025-00006-5","url":null,"abstract":"<p><p>Identification of novel drug targets is a key component of modern drug discovery. While antimalarial targets are often identified through the mechanism of action studies on phenotypically derived inhibitors, this method tends to be time- and resource-consuming. The discoverable target space is also constrained by existing compound libraries and phenotypic assay conditions. Leveraging recent advances in protein structure prediction, we systematically assessed the <i>Plasmodium falciparum</i> genome and identified 867 candidate protein targets with evidence of small-molecule binding and blood-stage essentiality. Of these, 540 proteins showed strong essentiality evidence and lack inhibitors that have progressed to clinical trials. Expert review and rubric-based scoring of this subset based on additional criteria such as selectivity, structural information, and assay developability yielded 27 high-priority antimalarial target candidates. This study also provides a genome-wide data resource for <i>P. falciparum</i> and implements a generalizable framework for systematically evaluating and prioritizing novel pathogenic disease targets.</p>","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"2 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11892419/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
NPJ drug discoveryPub Date : 2025-01-01Epub Date: 2025-04-02DOI: 10.1038/s44386-025-00008-3
Amit K Verma, Robbert Q Kim, Dirk A Lamprecht, Clara Aguilar-Pérez, Sarah Wong, Nicolas Veziris, Alexandra Aubry, José M Bartolomé-Nebreda, Rodrigo J Carbajo, Jennefer Wetzel, Meindert H Lamers
{"title":"Structural and mechanistic study of a novel inhibitor analogue of <i>M. tuberculosis</i> cytochrome bc<sub>1</sub>:aa<sub>3</sub>.","authors":"Amit K Verma, Robbert Q Kim, Dirk A Lamprecht, Clara Aguilar-Pérez, Sarah Wong, Nicolas Veziris, Alexandra Aubry, José M Bartolomé-Nebreda, Rodrigo J Carbajo, Jennefer Wetzel, Meindert H Lamers","doi":"10.1038/s44386-025-00008-3","DOIUrl":"10.1038/s44386-025-00008-3","url":null,"abstract":"<p><p>Drug-resistant tuberculosis (TB) continues to challenge treatment options, necessitating the exploration of new compounds of novel targets. The mycobacterial respiratory complex cytochrome bc<sub>1</sub>:aa<sub>3</sub> has emerged as a promising target, exemplified by the success of first-in-class inhibitor Q203 in phase 2 clinical trials. However, to fully exploit the potential of this target and to identify the best-in-class inhibitor more compounds need evaluation. Here, we introduce JNJ-2901, a novel Q203 analogue, that demonstrates activity against multidrug-resistant <i>M. tuberculosis</i> clinical strains at sub-nanomolar concentration and 4-log reduction in bacterial burden in a mouse model of TB infection. Inhibitory studies on purified enzymes validate the nanomolar inhibitions observed in mycobacterial cells. Additionally, cryo-EM structure analysis of cytochrome bc<sub>1</sub>:aa<sub>3</sub> bound to JNJ-2901 reveals the binding pocket at the menaquinol oxidation site (Qp), akin to other substate analogue inhibitors like Q203 and TB47. Validation of the binding site is further achieved by generating and isolating the JNJ-2901 resistant mutations in <i>M. tuberculosis</i>, followed by purification and resistance analysis of the resistant cytochrome bc<sub>1</sub>:aa<sub>3</sub> complex. Our comprehensive work lays the foundation for further clinical validations of JNJ-2901.</p>","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"2 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11964921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
NPJ drug discoveryPub Date : 2025-01-01Epub Date: 2025-05-12DOI: 10.1038/s44386-025-00011-8
Brian E Krumm, Bryan L Roth
{"title":"Intracellular GPCR modulators enable precision pharmacology.","authors":"Brian E Krumm, Bryan L Roth","doi":"10.1038/s44386-025-00011-8","DOIUrl":"10.1038/s44386-025-00011-8","url":null,"abstract":"<p><p>G-protein-coupled receptors (GPCRs) have proven to be the most successful target class for drug discovery but their complicated signal transduction pathways cause difficulties for drug development. Recently, ligands have been identified that engage an intracellular binding site which promotes pathway biased signal in cooperation with orthosteric ligands. Here, we explore the topic of biased signaling and intracellular modulators to understand their application for precision pharmacology of Class A or Rhodopsin-Like GPCRs.</p>","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"2 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12069105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
NPJ drug discoveryPub Date : 2025-01-01Epub Date: 2025-10-01DOI: 10.1038/s44386-025-00027-0
Robert Chen, Áine Duffy, Joshua K Park, David Stein, Avner Schlessinger, Yuval Itan, Benjamin S Glicksberg, Rory J Tinker, Daniel M Jordan, Ghislain Rocheleau, Ron Do
{"title":"Genetic evidence informs the direction of therapeutic modulation in drug development.","authors":"Robert Chen, Áine Duffy, Joshua K Park, David Stein, Avner Schlessinger, Yuval Itan, Benjamin S Glicksberg, Rory J Tinker, Daniel M Jordan, Ghislain Rocheleau, Ron Do","doi":"10.1038/s44386-025-00027-0","DOIUrl":"10.1038/s44386-025-00027-0","url":null,"abstract":"<p><p>Determining the correct direction of effect (DOE), whether to increase or decrease the activity of a drug target, is essential for therapeutic success. We introduce a framework to predict DOE at gene and gene-disease levels using gene and protein embeddings and genetic associations across the allele frequency spectrum, respectively. Specifically, we predict: (1) DOE-specific druggability for 19,450 protein-coding genes with a macro-averaged area under the receiver operating characteristic curve (AUROC) of 0.95; (2) isolated DOE among 2553 druggable genes with a macro-averaged AUROC of 0.85; and (3) gene-disease-specific DOE for 47,822 gene-disease pairs with a macro-averaged AUROC of 0.59, with performance improving with genetic evidence availability. Our predictions outperform existing approaches, are associated with clinical trial success, and identify novel therapeutic opportunities. We uncover genetic and functional differences between activator and inhibitor targets, allowing DOE inference independent of disease context. This framework represents a valuable tool for target selection and drug development.</p>","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"2 1","pages":"24"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12488494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
NPJ drug discoveryPub Date : 2025-01-01Epub Date: 2025-12-01DOI: 10.1038/s44386-025-00031-4
Jordy Homing Lam, Vsevolod Katritch
{"title":"Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches.","authors":"Jordy Homing Lam, Vsevolod Katritch","doi":"10.1038/s44386-025-00031-4","DOIUrl":"10.1038/s44386-025-00031-4","url":null,"abstract":"<p><p>Structure-based drug design is rapidly evolving, driven by advances in both physics-based and knowledge-based methods. These computational approaches are increasingly integrated across all stages of drug discovery. Despite remarkable progress, challenges remain in achieving accuracy, generalizability, computational efficiency, and chemical synthesizability. In this review, we provide a critical overview of advances, strengths, and limitations of recent methods. We also discuss synergies between the two concepts that hold promises for future advancements towards their practical applicability.</p>","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"2 1","pages":"29"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12669043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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