Journal of chemical information and modeling
40293160
Retroviruses such as HIV cause significant diseases in humans and other organisms, making the discovery of antiretroviral (ARV) drugs a critical priority. While databases like ChEMBL contain valuable information, their complexity poses challenges. Th...
Recent advances in pharmacology are revolutionizing drug discovery and treatment strategies through personalized medicine, pharmacogenomics, and artificial intelligence (AI). The objective of the present study is to review the role of personalized me...
Journal of chemical information and modeling
40302701
Today's drug discovery increasingly relies on computational and machine learning approaches to identify novel candidates, yet data scarcity remains a significant challenge. To address this limitation, we present , an application specifically designed...
, a single-cell parasite prevalent in tropical and subtropical regions worldwide, can cause varying degrees of leishmaniasis, ranging from self-limiting skin lesions to potentially fatal visceral complications. As such, the parasite has been the subj...
We are living in an era in which AI technology has become widely available and accessible to many people. The field of drug discovery is no exception, and many pharmaceutical companies have actually begun to utilize AI technology in drug discovery re...
Deep learning (DL) methods have drastically advanced structure-based drug discovery by directly predicting protein structures from sequences. Recently, these methods have become increasingly accurate in predicting complexes formed by multiple protein...
Journal of computer-aided molecular design
40285895
Predicting molecular toxicity is an important stage in the process of drug discovery. It is directly related to medical destiny and human health. This paper presents an enhanced model for chemical respiratory toxicity prediction. It used a combinatio...
The development of universal machine learning potentials (MLP) for small organic and drug-like molecules requires large, accurate datasets that span diverse chemical spaces. In this study, we introduce the QDπ dataset which incorporates data taken fr...
Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in clinical trials. Deep learning advancements, particularly generative models, offer potential solutions...
Journal of molecular graphics & modelling
40245571
Parallel artificial membrane permeability assay (PAMPA) is widely used in the early phases of drug discovery as it is quite robust and offers high throughput. It serves as a platform for assessing the permeability and absorption of pharmaceutical com...