Conventional wet laboratory testing, validations, and synthetic procedures are costly and
time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques …

The purpose of this study was to discuss how artificial intelligence (AI) methods have
affected the field of drug development. It looks at how AI models and data resources are …

Prostate cancer (PCa) is a major leading cause of mortality of cancer among males. There
have been numerous studies to develop antagonists against androgen receptor (AR), a …

BRAF kinase inhibitors have shown promise in treating melanoma patients with BRAF-V600
mutations. However, their clinical benefits are limited due to the development of acquired …

Reliable molecular property prediction is essential for various scientific endeavors and
industrial applications, such as drug discovery. However, the data scarcity, combined with …

Cytochrome P450 17A1 (CYP17A1) is one of the key enzymes in steroidogenesis that
produces dehydroepiandrosterone (DHEA) from cholesterol. Abnormal DHEA production …

Tetrahydropyrazolo [1, 5-a] pyrimidine (THPP) is an attractive scaffold for designing
biologically active compounds. The most obvious way to obtain such compounds is to …

Off-target drug interactions are a major reason for candidate failure in the drug discovery
process. Anticipating potential drug's adverse effects in the early stages is necessary to …

This paper deals with the need for artificial intelligence (AI) such as Convolutional Neural
Network (CNN), Recurrent Neural Network (RNN), Natural Language Processing (NLP) …

The search for novel therapeutic compounds remains an overwhelming task owing to the
time-consuming and expensive nature of the drug development process and low success …