Researchers uncover use of AI for drug discovery


To comprehend the interaction between drug and target is a key research area in drug discovery wherein the interaction refers to the interaction between chemical compounds and protein targets. According to estimations of chemists, 1060 compounds with drug-like properties can be manufactured. This is more than the total number of atoms in the Solar System, as reported in a n article in the journal Nature.

On an average, drug development takes about 14 years and costs up to 1.5 billion dollars. In the vast galaxy, during drug discovery, apparently traditional biological experiments for DTI identification are normally expensive and time-intensive.

In the past decade, an expert in computer-aided drug and design at the Zhejiang University College of Pharmaceutical Sciences has been involved in developing drugs using computer technology. The key challenge lies in interactions between drug molecules and unknown targets. The discovery of drugs more efficiently involves development of a new breakthrough method.

Recently, AI has opened new possibilities for such endeavors. The use of AI is likely to facilitate to be able to reach more upstream phase in drug discovery, and thus improve efficiency and success rage of drug development.

Besides AI, multi-omics data such as proteomics, genomics, and pharmacology have also flourished. The information about proteins, drugs, side effects, molecular functions, biological processes, biological enzymes, ion channels, and cellular components have been stored in specialized databanks. However, the use of these parameters for drug discovery remains clear.

Meanwhile, the domain of big data and network science is particularly suited for inter-disciplinary research. This considerable body of biological information can be teamed into a multi-layered heterogeneous network system.

Earlier, in Nov 2021, a team co-published a research article published in the journal Nature Communications.

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