Role of Artificial Intelligence in Revolutionizing Drug Development

AI integrated with wet lab research enhances cancer drug discovery by predicting protein structures to personalize treatment through multiomics.

Highlights:

AI accelerates drug discovery by identifying effective compounds for complex diseases like cancer
Multi-omics enhances cancer treatment precision by integrating diverse biological data for personalized therapies
AI and wet-lab research is crucial for validating predictions and conducting essential tests

Drug development is the process of bringing a new pharmaceutical drug into clinical practice. To develop a drug with efficacy it is important to identify the lead compound. With advancements in artificial intelligence (AI), drug discovery and development are done with precision ().

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‘Did You Know?
Artificial Intelligence (AI) in #drugdiscovery has predicted the structures of 200 million known proteins. #drugforcancer #AI #medindia’

AI's Role in Cancer Drug Discovery

AI integrated with drug development accelerates drug candidate identification for complex diseases like cancer by analyzing large datasets and biological interactions. The AI model is capable of predicting the complex structures of proteins from their amino acid sequences. The model has successfully predicted the structure of almost all 200 million known proteins.

Traditional drug development has often relied on trial-and-error processes in laboratories. While AI rapidly analyzes biological data to identify the potential drug for treatment. Researchers can now use AI algorithms to find effective drug compounds in just weeks, which usually takes months or even years.

This speed is crucial, especially in cancer research, where treatments often damage healthy cells along with malignant ones. AI-based tools are also improving the early detection of diseases like ovarian cancer, by analyzing genetic changes and protein biomarkers in blood tests.

Clinical Trials - Early History
Many of the drugs that are used today have been discovered by chance or often by mere serendipity

Proteins and Multiomics in AI Cancer Research

Proteins play a crucial role in disease progression. An AI model has to be trained on a large set of data for its efficiency. The AlphaFold2 model was developed by training it on all known amino acid sequences paired with their determined protein structures.

Then the protein data is applied in drug development with multiomics. Multiomics is a technique used to analyze multiple datasets like genomics, epigenomics, proteomics, microbiome, metabolome, and transcriptomics. Advanced systems combine various types of biological and textual data like genetic sequences, 3D models of molecules, structured biological knowledge, and patient records to enhance the precision of personalized treatment for cancer.

Artificial Intelligence Helps in Drug Discovery
Artificial intelligence could transform drug development by searching for disease targets, then predicting if a drug will be successful.

AI in Biologics for Cancer Therapy

Biologics are drugs made from living organisms and are harder to design than small-molecule drugs due to their larger and more complex structures. While small molecules can be designed directly using AI, biologics require advanced computational techniques to identify effective drug design. However, AI has already contributed to the discovery of 50–60 biologics that are currently under development, with many focused on cancer treatment.

Many pharmaceutical companies are using AI and multiomics to develop cancer therapies. ImmunoPrecise Antibodies, a biotechnology company used AI to create bispecific antibodies targeting cancer cells in the tumor microenvironment. Similarly, BostonGene developed an AI-powered platform that finds suitable therapies for patients.

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Why AI Cannot Replace Wet-Lab Work

While AI is transforming pharmaceutical research, it cannot replace traditional wet lab experiments. Laboratory works are essential for validating AI-generated predictions and to conduct tests. Human knowledge and critical thinking also play an important role in drug discovery and development. AI depends on the data found by researchers after conducting many trial-and-error studies. Thus AI and wet-lab research are both equally important for innovation in drug development.

Reference:

Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration - (https://pmc.ncbi.nlm.nih.gov/articles/PMC10546458/)

Source-Medindia