Perturbation-Theory Machine Learning for Multi-Target Drug Discovery in Modern Anticancer Research.

Area of Science:

• Computational chemistry
• Drug discovery
• Oncology

Background:

• Cancers are complex diseases with high mortality, often evading immune responses and developing drug resistance.
• Current anticancer drug discovery faces challenges due to the multi-factorial nature of cancer and limitations in computational methods.
• There is a need for novel anticancer agents with multi-target actions and improved efficacy and safety.

Purpose of the Study:

• To review the development and application of perturbation-theory machine learning (PTML) in multi-target anticancer drug discovery.
• To highlight PTML's potential in overcoming limitations of existing computational methods.
• To explore PTML's role in discovering versatile small-molecule anticancer agents.

Main Methods:

• Review of investigations on PTML modeling over the past decade.
• Analysis of PTML's capability in handling complex datasets and multi-target predictions.
• Discussion of PTML's interpretability and versatility in drug discovery.

Main Results:

• PTML emerges as a cutting-edge approach for multi-target drug discovery in cancer research.
• PTML addresses limitations such as homogeneous datasets, single-target prediction, and lack of interpretability.
• The approach shows significant promise for identifying versatile anticancer agents.

Conclusions:

• PTML modeling is a powerful tool for accelerating the discovery of novel anticancer agents.
• This approach facilitates the development of drugs with multi-target modes of action.
• Future applications of PTML in oncology drug discovery are promising.