Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource cons...
BACKGROUND: Identification of drug target interactions (DTI) is an important part of the drug discovery process. Since prediction of DTI using laboratory tests is time consuming and laborious, automated tools using computational intelligence (CI) tec...
Journal of agricultural and food chemistry
Jul 15, 2025
Rapid evolution of digital technologies has enabled vital tools in pesticide discovery, which are crucial for agricultural productivity and food security. Therein, molecular editors have emerged as basic and critical tools in this field. However, exi...
Antiviral peptides (AVPs) hold great potential for combating viral infections, yet their discovery and development remain challenging. In this study, we present a hybrid model combining Wasserstein Generative Adversarial Networks with Gradient Penalt...
High-throughput screening (HTS) remains central to small molecule lead discovery, but increasing assay complexity challenges the screening of large compound libraries. While retrospective studies have assessed active-learning-guided screening, extens...
The process of drug discovery is intricate, and encompasses a series of detailed phases of research, development, and testing, aimed at evaluating the safety and effectiveness of prospective therapeutic agents. Artificial Intelligence has emerged as ...
The rise of antibiotic-resistant pathogens, particularly gram-negative bacteria, highlights the urgent need for novel therapeutics. Drug-resistant infections now contribute to approximately 5 million deaths annually, yet traditional antibiotic discov...
Drug repurposing accelerates microbial therapy development by bypassing the costly and time-consuming traditional drug discovery process. However, existing computational methods for predicting drug-microbe associations (MDAs) struggle to capture comp...
Lactate dehydrogenase A (LDHA) is a promising target for cancer therapy due to its crucial role in aerobic glycolysis. Despite extensive efforts, the structural diversity of LDHA inhibitors remains limited. Here, we utilized machine learning techniqu...
Journal of chemical information and modeling
Jul 9, 2025
In recent years, generative deep learning has emerged as a transformative approach in drug design, promising to explore the vast chemical space and generate novel molecules with desired biological properties. This perspective examines the challenges ...
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