Enhanced Isolation of Streptomyces from Different Soil Habitats in Calamba City, Laguna, Philippines using a Modified Integrated Approach by Jhon Wilson A. Antido, Fresthel Monica M. Climacosa

“…These Streptomyces isolates can be valuable for future drug discovery and development research.…”
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Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms by Sahar K. Hussin, Salah M. Abdelmageid, Adel Alkhalil, Yasser M. Omar, Mahmoud I. Marie, Rabie A. Ramadan

“…Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. …”
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Bioactivity-guided isolation and molecular modeling of the anti-inflammatory constituents from the leaves of Duranta erecta Linn. by Marina Sobhy, Farid N. Kirollos, Sameh F. AbouZid, Nasser S. M. Ismail, Riham A. El-Shiekh, Essam Abdel-Sattar

“…Overall, the data reported in this study add to our understanding of the pharmacological properties of the examined plant and Duranterectoside A (1) and pave the way for future research and investigation in inflammation and drug discovery.…”
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Evaluation of the Hair Cell Regeneration in Zebrafish Larvae by Measuring and Quantifying the Startle Responses by Changquan Wang, Zhenmin Zhong, Peng Sun, Hanbing Zhong, Hongzhe Li, Fangyi Chen

“…Together it demonstrated the capability of this behavioral assay in evaluating the hair cell functions of fish larvae and its potential as a high-throughput screening tool for auditory-related gene and drug discovery.…”
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Potential antiproliferative effect of isoxazolo- and thiazolo coumarin derivatives on breast cancer mediated bone and lung metastases by Ballazhi Lulzime, Popovski Emil, Jashari Ahmed, Imeri Faik, Ibrahimi Ibrahim, Mikhova Bozhana, Mladenovska Kristina

“…The study highlights the current progress in the development of coumarin scaffolds for drug discovery as novel anticancer agents in metastatic breast cancer. …”
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Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities by Sarat Kumar Chettri, Rup Kumar Deka, Manob Jyoti Saikia

“…It finds that the existing studies mostly used conventional machine learning (ML) algorithms and artificial neural networks (ANNs) for a variety of tasks, such as drug discovery, disease surveillance systems, early disease detection and diagnostic accuracy, and management of healthcare resources in India. …”
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MultiChem: predicting chemical properties using multi-view graph attention network by Heesang Moon, Mina Rho

“…Abstract Background Understanding the molecular properties of chemical compounds is essential for identifying potential candidates or ensuring safety in drug discovery. However, exploring the vast chemical space is time-consuming and costly, necessitating the development of time-efficient and cost-effective computational methods. …”
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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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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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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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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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