AIMC Topic: Drug Development

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In silico design strategies for tubulin inhibitors for the development of anticancer therapies.

Expert opinion on drug discovery
INTRODUCTION: Microtubules, composing of α, β-tubulin dimers, are important for cellular processes like proliferation and transport, thereby they become suitable targets for research in cancer. Existing candidates often exhibit off-target effects, ne...

Application of Artificial Intelligence in the Development of Traditional Chinese Medicine.

Basic & clinical pharmacology & toxicology
Traditional Chinese medicine (TCM) has long been recognized for its mild therapeutic effects, significant efficacy and minimal adverse reactions. However, challenges such as reliance on human expertise in TCM production and quality control, unclear c...

The dawn of a new era: can machine learning and large language models reshape QSP modeling?

Journal of pharmacokinetics and pharmacodynamics
Quantitative Systems Pharmacology (QSP) has emerged as a cornerstone of modern drug development, providing a robust framework to integrate data from preclinical and clinical studies, enhance decision-making, and optimize therapeutic strategies. By mo...

DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation.

Nature communications
Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug disco...

What patents on AI-derived drugs reveal.

Science (New York, N.Y.)
Less in-depth, in vivo testing before patenting may affect overall research and development.

Recent advances in antibody optimization based on deep learning methods.

Journal of Zhejiang University. Science. B
Antibodies currently comprise the predominant treatment modality for a variety of diseases; therefore, optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development. Inspired by the great success of a...

The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries.

AAPS PharmSciTech
In today's world, with an increasing patient population, the need for medications is increasing rapidly. However, the current practice of drug development is time-consuming and requires a lot of investment by the pharmaceutical industries. Currently,...

Leveraging large language models to compare perspectives on integrating QSP and AI/ML.

Journal of pharmacokinetics and pharmacodynamics
Two recent papers offer contrasting perspectives on integrating Quantitative Systems Pharmacology (QSP) and Artificial Intelligence/Machine Learning (AI/ML): one views QSP as the primary driver using AI/ML to enhance computational tasks, while the ot...

Advances and critical aspects in cancer treatment development using digital twins.

Briefings in bioinformatics
The emergence of digital twins (DTs) in the arena of anticancer treatment echoes the transformative impact of artificial intelligence in drug development. DTs provide dynamic, accessible platforms that may accurately replicate patient and tumor chara...

Artificial intelligence to predict inhibitors of drug-metabolizing enzymes and transporters for safer drug design.

Expert opinion on drug discovery
INTRODUCTION: Drug-metabolizing enzymes (DMEs) and transporters (DTs) play integral roles in drug metabolism and drug-drug interactions (DDIs) which directly impact drug efficacy and safety. It is well-established that inhibition of DMEs and DTs ofte...