Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …

The COVID-19 pandemic has emphasized the need for novel drug discovery process.
However, the journey from conceptualizing a drug to its eventual implementation in clinical …

Molecular representation learning (MRL) has gained tremendous attention due to its critical
role in learning from limited supervised data for applications like drug design. In most MRL …

This chapter comprehensively explores the pivotal role of artificial intelligence (AI) in drug
discovery and development, encapsulating its potentials, methodologies, real-world …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, develo** drugs for central nervous system (CNS) disorders remains the most …

A variety of machine learning methods such as naive Bayesian, support vector machines
and more recently deep neural networks are demonstrating their utility for drug discovery …

Artificial intelligence (AI) is a branch of computer science that allows machines to work
efficiently, can analyze complex data. The research focused on AI has increased …

Despite an abundance of online databases providing access to chemical data, there is
increasing demand for high-quality, structure-curated, open data to meet the various needs …

The collection of chemical structure information and associated experimental data for
quantitative structure–activity/property relationship (QSAR/QSPR) modeling is facilitated by …