AIMC Topic: Drug Discovery

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Novel drug discovery: Advancing Alzheimer's therapy through machine learning and network pharmacology.

European journal of pharmacology
Alzheimer's disease (AD), marked by tau tangles and amyloid-beta plaques, leads to cognitive decline. Despite extensive research, its complex etiology remains elusive, necessitating new treatments. This study utilized machine learning (ML) to analyze...

Good machine learning practices: Learnings from the modern pharmaceutical discovery enterprise.

Computers in biology and medicine
Machine Learning (ML) and Artificial Intelligence (AI) have become an integral part of the drug discovery and development value chain. Many teams in the pharmaceutical industry nevertheless report the challenges associated with the timely, cost effec...

Best practices for machine learning in antibody discovery and development.

Drug discovery today
In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ...

Geometric deep learning methods and applications in 3D structure-based drug design.

Drug discovery today
3D structure-based drug design (SBDD) is considered a challenging and rational way for innovative drug discovery. Geometric deep learning is a promising approach that solves the accurate model training of 3D SBDD through building neural network model...

A guide to artificial intelligence for cancer researchers.

Nature reviews. Cancer
Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden informati...

Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases.

Journal of chemical information and modeling
Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand ...

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.

Journal of chemical information and modeling
Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale dr...

Quantifying the Hardness of Bioactivity Prediction Tasks for Transfer Learning.

Journal of chemical information and modeling
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated w...