Natural products (NPs) produced by microorganisms and plants are a major source of drugs,
herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics …

Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of
bioactivity and the study of structure–activity relationships. Over the past decade, an …

Abstract Concepts of individualized therapy in the 1970s and 1980s attempted to develop
predictive in vitro tests for individual drug responsiveness without reaching clinical routine …

Time span in literature: 1985-early 2024Natural products play a key role in drug discovery,
both as a direct source of drugs and as a starting point for the development of synthetic …

Phytocompounds are a well-established source of drug discovery due to their unique
chemical and functional diversities. In the area of cancer therapeutics, several …

Precision medicine entails the design of therapies that are matched for each individual
patient. Thus, predictive modeling of drug responses for specific patients constitutes a …

Rationale Natural products have been great sources for drug discovery. However, the
structures of natural products are diverse and difficult to elucidate. Cordyceps militaris is a …

The purpose of our study was to bring elements of knowledge on the anticancer and
antibacterial effects of two plants, widely used in Moroccan traditional pharmacopeia. These …

Many machine learning techniques are used as drug discovery tools with the intent to speed
characterization by determining relationships between compound structure and biological …

The identification of mutation markers and the selection of appropriate treatment for patients
with specific genome mutations are important steps in the development of targeted therapies …