Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data by Ísak Valsson, Matthew T. Warren, Charlotte M. Deane, Aniket Magarkar, Garrett M. Morris, Philip C. Biggin

“…However, current models lack robust evaluation and fail on tasks encountered in (hit-to-) lead optimisation, such as ranking the binding affinity of a congeneric series of ligands, thereby limiting their application in drug discovery. Here, we address these issues by first introducing a novel attention-based graph neural network model called AEV-PLIG (atomic environment vector–protein ligand interaction graph). …”
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Exploiting question-answer framework with multi-GRU to detect adverse drug reaction on social media by Jiao-huang Luo, Ai-hua Yang

“…Abstract Adverse Drug Reactions (ADRs) stand out as a pressing challenge in public health and a critical aspect of drug discovery. The dilemma arises from the inherent impossibility of conducting a comprehensive evaluation of a drug before its market release, constrained by the limitations in scale and duration of clinical trials. …”
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Effects of atractylodin and β-Eudesmol on P-glycoprotein and Caco-2 cells permeability by Artitaya Thiengsusuk, Wiriyaporn Sumsakul, Kesara Na–Bangchang

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Harnessing the immunomodulatory potential of natural products in precision medicine—a comprehensive review by Maya G. Pillai, Helen Antony

“…NPs offer a rich source of potential drug candidates, driving innovation in drug discovery. Recent breakthroughs have reignited interest in harnessing the therapeutic benefits of natural compounds. …”
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Divergent alkynylative difunctionalization of amide bonds through C–O deoxygenation versus C–N deamination by Feng Liu, Xueyuan Yan, Fangfang Cai, Wenjuan Hou, Jianyu Dong, Shuang-Feng Yin, Genping Huang, Tieqiao Chen, Michal Szostak, Yongbo Zhou

“…Abstract The transformation and utilization of amides are significant in organic synthesis and drug discovery. Here we demonstrate a divergent alkynylative difunctionalization of amides in a single transformation. …”
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Understanding integrative approach of translational bioinformatics on cardiovascular disease: Myocardial Ischemia by Yeswanth Ranganathan, Saayaa Nazar, Ravi Shankar Krishnan, Yuvaraj Dinakarkumar, Vijayalakshmi Varadarajan, Lenita Sebastian, Brindha Rethinam

“…The study highlights the impact of personalized medicine, drug discovery, and repurposing through bioinformatics. …”
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InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks by Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi

“…Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance. …”
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Screening and evaluation of novel DPP-IV inhibitory peptides in goat milk based on molecular docking and molecular dynamics simulation by Kuo Dang, Jing Lan, Yanli Wang, Daodong Pan, Lihui Du, Shikun Suo, Yali Dang, Xinchang Gao

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Evolution of artificial intelligence in healthcare: a 30-year bibliometric study by Yaojue Xie, Yuansheng Zhai, Yuansheng Zhai, Guihua Lu, Guihua Lu

“…Notably, the rapid expansion of emerging topics such as COVID-19 and new drug discovery in recent years is noteworthy. Furthermore, the top five most cited papers in 2023 were all pertinent to the theme of ChatGPT.ConclusionThis study reveals a sustained explosive growth trend in AI technologies within the healthcare sector in recent years, with increasingly profound applications in medicine. …”
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Evaluation of Anti-Cancer, Pro-Apoptotic, and Anti-Metastatic Effects of Synthetic Analogue Compound 2-Imino-7-Methoxy-4-(4-fluorophenyl)-2H-1,3-Thiazino [3,2-a] Benzimidazole on P... by Mpho Ndou, Marthe C. D. Fotsing, Michael H. K. Kengne, Edwin M. Mmutlane, Derek T. Ndinteh, Mthokozisi B.C. Simelane, Lesetja Motadi, Mpho Choene

“… Synthetic compounds are widely used in cancer drug discovery. Chemotherapies aim to inhibit proliferation and induce apoptosis however, they have limitations. …”
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KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning by Huimin Luo, Huimin Luo, Hui Yang, Hui Yang, Ge Zhang, Ge Zhang, Jianlin Wang, Jianlin Wang, Junwei Luo, Chaokun Yan, Chaokun Yan, Chaokun Yan

“…Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. …”
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GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity by Junwei Luo, Ziguang Zhu, Zhenhan Xu, Chuanle Xiao, Jingjing Wei, Jiquan Shen

“…Abstract Background Drug-target binding affinity (DTA) prediction is vital in drug discovery and repositioning, more and more researchers are beginning to focus on this. …”
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DockCADD: A streamlined in silico pipeline for the identification of potent ribosomal S6 Kinase 2 (RSK2) inhibitors by El Mehdi Karim, Meriem Khedraoui, Abdelkbir Errougui, Yasir S. Raouf, Abdelouahid Samadi, Samir Chtita

“…These results have pointed out the use of DockCADD as an efficient tool for the fast and low-cost process of drug discovery; L1–L3 should be further validated experimentally for cancer therapy.…”
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Heterogeneous graph convolutional neural network for protein-ligand scoring by Kevin Crampon, Alexis Giorkallos, Xavier Vigouroux, Stephanie Baud, Luiz Angelo Steffenel

“…Aim: Drug discovery is a long process, often taking decades of research endeavors. …”
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Anthelmintic activity of 1,10-phenanthroline-5,6-dione-based metallodrugs by Maria E. Cirino, Thainá R. Teixeira, Alessandro M. H. Silva, Ana C. C. Borges, Lucas Fukui-Silva, Luis G. Wagner, Christiane Fernandes, Malachy McCann, André L. S. Santos, Josué de Moraes

“…Additionally, the compounds were tested on Caenorhabditis elegans, a model organism for drug discovery. All compounds exhibited strong antiparasitic activity, with Cu-phendione showing the greatest potency (EC50 = 2.3 µM for S. mansoni and 6.4 µM for A. cantonensis). …”
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Regulation of adipocyte differentiation and lipid metabolism by novel synthetic chromenes exploring anti-obesity and broader therapeutic potential by Kewalin Inthanon, Natthawut Wong-a-nan, Srisulak Dheeranupattana, Andres Garcia Guerra, Neal M. Davies, Chatchai Kesornpun, Sasithorn Sangher, Prasat Kittakoop

“…This study presents several promising prospects for the development of synthetic chromenes as potential anti-obesity agents, opening new avenues for drug discovery and benefitting individuals worldwide in addressing obesity-related challenges to human health. …”
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The research progress on effective connectivity in adolescent depression based on resting-state fMRI by Xuan Deng, Jiajing Cui, Jinyuan Zhao, Jinji Bai, Junfeng Li, Kefeng Li

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Synthetic peptides as valuable and versatile tools for research: our 20 year journey in Chile by Constanza Cárdenas, Paula Santana, Claudio Álvarez, Luis Mercado, Sergio Marshall, Fernando Albericio, Fanny Guzmán

“…One particularly relevant area is in drug discovery, offering alternatives to face the emergence of antibiotic-resistant microorganisms. …”
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Comparative analysis of ocular biometrics using spectral domain optical coherence tomography with Purkinje image and optic nerve head alignments in mice by Ying Hon Sze, Bing Zuo, Da Qian Lu, King Kit Li, Dennis Yan Yin Tse, Qian Zhao, Thomas Chuen Lam

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PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports by Javier Corvi, Nicolás Díaz-Roussel, José M. Fernández, Francesco Ronzano, Emilio Centeno, Pablo Accuosto, Celine Ibrahim, Shoji Asakura, Frank Bringezu, Mirjam Fröhlicher, Annika Kreuchwig, Yoko Nogami, Jeong Rih, Raul Rodriguez-Esteban, Nicolas Sajot, Joerg Wichard, Heng-Yi Michael Wu, Philip Drew, Thomas Steger-Hartmann, Alfonso Valencia, Laura I. Furlong, Salvador Capella-Gutierrez

“…Text mining technologies have the ability to automatically extract, analyse and aggregate such information, providing valuable new insights into the drug discovery and development process. In the context of the eTRANSAFE project, we present PretoxTM (Preclinical Toxicology Text Mining), the first system specifically designed to detect, extract, organise and visualise treatment-related findings from toxicology reports. …”
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