How AI is transforming Drug Discovery?

Artificial intelligence (AI) is a technical system created to resemble human intelligence. It consists of some fields including knowledge representation, solution search, machine learning, and reasoning. With the help of the system, software, tools, and network, AI can analyze and gain knowledge from the input data to make independent decisions.

Artificial intelligence (AI) can be applied at several stages of the drug development process, from drug design to drug screening. As the need to cut the overall cost and length of drug development grows, the industry is expanding quickly. In the field of drug discovery, there are enormous amounts of molecular data and literature. AI enables quick screening of the necessary data. This causes the AI business to grow and see increased acceptance. According to a research report by Astute Analytica, the Global Artificial Intelligence in Drug Discovery Market growing at a compound annual growth rate (CAGR) of 25% over the projection period from 2023 to 2030.

Application areas of drug discovery:

Lead optimization and Compound Screening

Compound screening and the lead optimization process are used in high throughput screening, Combinatorial chemistry, and virtual screening to pick drug candidates.

The compound database for AI-based Virtual Screening is created by extracting large amounts of compounds from freely accessible chemogenomics libraries, which contain tens of millions of compounds annotated with structural information. This approach enables medicinal chemists to rapidly identify possible lead molecules among millions using Naive Bayesian Classifiers, k-Nearest Neighbours, Support Vector Machines, Random Forests, and artificial neural network techniques.

Preclinical Studies

Preclinical studies, also known as non-clinical studies, are in-vitro and in vivo laboratory experiments for new therapeutic compounds to determine their safety and efficacy profile.

An unsupervised method of clustering-based machine learning tools analyses RNA sequencing technologies to determine a molecular mechanism of action, speeding up the process of gathering pertinent large amounts of biological data. Additionally, it reveals numerous hitherto unrecognized connections between various stimuli and the cytokines they influence.

Clinical Trials

Clinical trial AI tool development would be great for detecting patient disorders, locating gene targets, forecasting the outcome of a created chemical, and on- and off-targets. One AI smartphone application improved drug adherence by 25% in Phase II clinical trials as compared to conventional direct observation therapy.

Validation and Target Selection

Target identification focuses on determining the function of potential molecular targets (genes/proteins of a small molecule) and their contribution to a disease to identify the efficacy target of treatment. structural genomics, functional genomics, proteomics, in-vitro cell-based assays, and in-vivo animal research assays must all be considered during selection and validation.