AI Medical Compendium Journal:
Drug discovery today

Showing 51 to 60 of 107 articles

Data considerations for predictive modeling applied to the discovery of bioactive natural products.

Drug discovery today
Natural products (NPs) constitute a large reserve of bioactive compounds useful for drug development. Recent advances in high-throughput technologies facilitate functional analysis of therapeutic effects and NP-based drug discovery. However, the larg...

From traditional to data-driven medicinal chemistry: A case study.

Drug discovery today
Artificial intelligence (AI) and data science are beginning to impact drug discovery. It usually takes considerable time and efforts until new scientific concepts or technologies make a transition from conceptual stages to practical applicability and...

Multimodal molecular imaging in drug discovery and development.

Drug discovery today
In addition to individual imaging techniques, the combination and integration of several imaging techniques, so-called multimodal imaging, can provide large amounts of anatomical, functional, and molecular information accelerating drug discovery and ...

Machine learning to design antimicrobial combination therapies: Promises and pitfalls.

Drug discovery today
Combination therapies can overcome antimicrobial resistance (AMR) and repurpose existing drugs. However, the large combinatorial space to explore presents a daunting challenge. In response, machine learning (ML) algorithms are being applied to identi...

Machine Learning guided early drug discovery of small molecules.

Drug discovery today
Machine learning (ML) approaches have been widely adopted within the early stages of the drug discovery process, particularly within the context of small-molecule drug candidates. Despite this, the use of ML is still limited in the pharmacokinetic/ph...

Oncological drug discovery: AI meets structure-based computational research.

Drug discovery today
The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the di...

Compound-protein interaction prediction by deep learning: Databases, descriptors and models.

Drug discovery today
The screening of compound-protein interactions (CPIs) is one of the most crucial steps in finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address intrinsic limitations of traditional HTS and virtual screening with th...

Defining clinical outcome pathways.

Drug discovery today
Here, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: mole...

Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives.

Drug discovery today
Molecular modeling in pharmacology is a promising emerging tool for exploring drug interactions with cellular components. Recent advances in molecular simulations, big data analysis, and artificial intelligence (AI) have opened new opportunities for ...

Enhancing preclinical drug discovery with artificial intelligence.

Drug discovery today
Artificial intelligence (AI) is becoming an integral part of drug discovery. It has the potential to deliver across the drug discovery and development value chain, starting from target identification and reaching through clinical development. In this...