Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some
protagonists point to vast opportunities potentially offered by such tools, others remain …

Convolutional neural networks (CNNs) have been used to extract information from various
datasets of different dimensions. This approach has led to accurate interpretations in several …

Identifying drug-target interactions will greatly narrow down the scope of search of candidate
medications, and thus can serve as the vital first step in drug discovery. Considering that in …

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

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead
optimization in drug discovery research, requires molecular representation. Previous reports …

Cancer treatments always face challenging problems, particularly drug resistance due to
tumor cell heterogeneity. The existing datasets include the relationship between gene …

Developments in computational omics technologies have provided new means to access
the hidden diversity of natural products, unearthing new potential for drug discovery. In …

Deep learning is becoming increasingly relevant in drug discovery, from de novo design to
protein structure prediction and synthesis planning. However, it is often challenged by the …

By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the
entire drug discovery process stands to undergo a profound transformation, offering a …

Science, and the way we undertake research, is changing. The increasing rate of data
generation across all scientific disciplines is providing incredible opportunities for data …