Dear colleagues,

Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it

provides more accurate and efficient methods for identifying the potentially toxic effects of

new compounds before they are tested in human clinical trials, thus saving time and money.

Various AI tools are used for toxicity prediction which includes machine learning models –

deep learning, neural networks, quantitative structure activity relationship, and molecular

docking. AI can detect diseases earlier through medical imaging analysis, predictive analytics,

wearable devices, remote monitoring and early disease risk assessment.

One of the key applications of AI in medicinal chemistry is the prediction of the efficacy and

toxicity of potential drug compounds. Classical protocols of drug discovery often rely on

labor-intensive and time-consuming experimentation to assess the potential effects of a

compound on the human body.

However, a host of AI tools are revolutionizing nearly every stage of the drug discovery

process, offering substantial potential to reshape the speed and economics of the industry.

Forensic sciences have also embraced AI, augmenting investigation techniques and

contributing to more effective criminal justice systems. AI algorithms can process vast

amounts of data, such as fingerprints, DNA profiles, and surveillance footage, aiding in

identifying suspects and investigating leads.

AI is here and here to stay; 50% of global healthcare companies plan to implement AI

strategies by 2027. For pharmaceutical manufacturers, AI has the potential to revolutionize

process design and control, and thus bring benefits to patients and challenges to regulators.

Comprehensive studies are needed so that researchers can understand toxicity prediction and

pave the way for new drug discovery methods.