A data-driven group retrosynthesis planning model inspired by neurosymbolic programming by Xuefeng Zhang, Haowei Lin, Muhan Zhang, Yuan Zhou, Jianzhu Ma

“…Abstract Deep generative models have garnered significant attention for their efficiency in drug discovery, yet the synthesis of proposed molecules remains a challenge. …”
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EdgeSecureDP: Strengthening IoHTs Differential Privacy Through Graphvariate Skellam by Mohamed Amjath, Shagufta Henna

“…These mechanisms treat data points independently, failing to account for the complex interconnections between nodes (drugs) and edges (interactions), leaves the network vulnerable to structural attacks that can reverse-engineer relationships, thus limiting the security of collaborative drug discovery. To address these limitations, this work proposes Graphvariate Skellam, a novel DP approach that leverages graph structure information in FL settings, referred to as EdgeSecureDP. …”
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Identification of egg protein-derived peptides as xanthine oxidase inhibitors: virtual hydrolysis, molecular docking, and in vitro activity evaluation by Zhipeng Yu, Yaxin Cao, Ruotong Kan, Huizhuo Ji, Wenzhu Zhao, Sijia Wu, Jingbo Liu, David Shiuan

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Drugs from poisonous plants: Ethnopharmacological relevance to modern perspectives by Bhagya Lakhmi Rajbongshi, Ashis K. Mukherjee

“…A few of these plants are emphasized, which have been tremendously explored and studied, hold significant potential to contribute to modern drug discovery. Furthermore, it addresses the possible prospects and challenges of using poisonous plants and their phytochemicals as therapeutic agents. …”
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Old and Recent Advances in Life Cycle, Pathogenesis, Diagnosis, Prevention, and Treatment of Malaria Including Perspectives in Ethiopia by Dejen Nureye, Solomon Assefa

“…In addition to previously known targets for diagnostic tool, vaccine and drug discovery scientists from all corner of the world are in search of new targets and chemical entities.…”
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Organoid development and applications in gynecological cancers: the new stage of tumor treatment by Yang Li, Meiying Qin, Ning Liu, Chunmei Zhang

“…This review provides an overview of recent advancements in the development of gynecologic cancer organoid models, highlighting their contributions to understanding disease mechanisms, facilitating drug discovery, and advancing precision medicine. It also addresses the potential and challenges of organoid technology, with a focus on its role in advancing personalized treatment approaches for GCs. …”
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Chemistry and pharmacological diversity of quinoxaline motifs as anticancer agents by Ajani Olayinka O., Nlebemuo Martins T., Adekoya Joseph A., Ogunniran Kehinde O., Siyanbola Tolutope O., Ajanaku Christiana O.

“…Medicinal chemistry researchers and pharmacists have unveiled quinoxaline templates as precursors of importance and valuable intermediates in drug discovery because they have been established to possess diverse pharmacological potentials. …”
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Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation by Yue Wan, Jialu Wu, Tingjun Hou, Chang-Yu Hsieh, Xiaowei Jia

“…Abstract Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. …”
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Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation by Matvei Anoshin, Asel Sagingalieva, Christopher Mansell, Dmitry Zhiganov, Vishal Shete, Markus Pflitsch, Alexey Melnikov

“…The new hybrid quantum machine learning algorithms, as well as the achieved scores of pharmacokinetic properties, contribute to the development of fast and accurate drug discovery processes.…”
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Multicomponent reactions driving the discovery and optimization of agents targeting central nervous system pathologies by Lucía Campos-Prieto, Aitor García-Rey, Eddy Sotelo, Ana Mallo-Abreu

“…Multicomponent reactions (MCRs) have emerged as powerful tools in accelerating drug discovery, enabling the rapid generation of chemical libraries with high diversity in a time-efficient and environmentally sustainable manner. …”
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Unlocking biological complexity: the role of machine learning in integrative multi-omics by Ravindra Kumar, Rajrani Ruhel, Andre J. van Wijnen

“…By integrating machine learning in multi-omics, we can enhance our understanding of drug discovery, disease, pathway, and network analysis. …”
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Molecular docking and quantitative structure-activity relationships for a series of Trypanosoma cruzi dihydroorotate dehydrogenase inhibitors by Gabriela Ciffeli de Jesus, Tatiana Santana Ribeiro, Adriano D. Andricopulo, Leonardo Luiz Gomes Ferreira

“…Essential for the survival of T. cruzi, the enzyme dihydroorotate dehydrogenase (DHODH) has become a key molecular target for drug discovery in Chagas disease. This study investigates the bi-dimensional and three-dimensional quantitative structure-activity relationships (QSAR) for a series of 64 T. cruzi DHODH inhibitors. …”
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Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network by Leixia Tian, Qi Wang, Zhiheng Zhou, Xiya Liu, Ming Zhang, Guiying Yan

“…Abstract In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are crucial in treatment for many diseases. …”
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Molecular techniques for cancer diagnostics by Vimal Kishor Singh, Ramesh Chandra

“…Recent advancements in describing genetic alterations in human cancers are a tempting reason for scientists to develop more effective, personalized therapies as the next level of cancer treatment. However, the drug discovery process is tedious, and getting approval from various regulatory authorities may be more frustrating for a steady developmental pace. …”
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Systematically developing a registry of splice-site creating variants utilizing massive publicly available transcriptome sequence data by Naoko Iida, Ai Okada, Yoshihisa Kobayashi, Kenichi Chiba, Yasushi Yatabe, Yuichi Shiraishi

“…Collectively, we provide a systematic approach for automatically acquiring a registry of SSCVs, which facilitates the elucidation of novel biological mechanisms underlying splicing and serves as a valuable resource for drug discovery. The catalogs of SSCVs identified in this study are accessible on the SSCV DB ( https://sscvdb.io ).…”
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Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation by Yishu Wang, Mengyao Guo, Xiaomin Chen, Dongmei Ai

“…Moreover, we evaluated the performance of these NLP-based generation models and another new model architecture based on a selective state space and selected the best approach jointing a transfer learning strategy for de novo drug discovery to target L858R/T790M/C797S-mutant EGFR in non-small cell lung cancer.…”
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Hyperuricemia-induced complications: dysfunctional macrophages serve as a potential bridge by Wenyi Gu, Wenyi Gu, Jiajing Zhao, Yu Xu, Yu Xu, Yu Xu

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QSPR Analysis of Some Alzheimer’s Compounds via Topological Indices and Regression Models by Muhammad Shoaib Sardar, Khalil Hadi Hakami

“…Topological indices and quantitative structure-property relationships (QSPRs) are indispensable in drug discovery. They allow researchers to analyze, compare, and predict the properties of chemical compounds, thereby expediting the identification of promising drug candidates while minimizing experimental costs and efforts. …”
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IUPHAR review: Drug repurposing in Schizophrenia – An updated review of clinical trials by Jihan K. Zaki, Jakub Tomasik, Sabine Bahn

“…Drug repurposing—the process of identifying new therapeutic uses for already approved compounds—offers a promising approach to overcoming the lengthy, costly, and high-risk process of traditional CNS drug discovery. This review aims to update our previous findings on the clinical drug repurposing pipeline in schizophrenia. …”
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Discovery of novel TACE inhibitors using graph convolutional network, molecular docking, molecular dynamics simulation, and Biological evaluation. by Muhammad Yasir, Jinyoung Park, Eun-Taek Han, Jin-Hee Han, Won Sun Park, Mubashir Hassan, Andrzej Kloczkowski, Wanjoo Chun

“…In conclusion, our study highlights the capability of deep learning models to enhance virtual screening efforts in drug discovery, efficiently identifying potential candidates for specific targets such as TACE. …”
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