Review on the use of artificial intelligence to predict suitable drugs (AIPD)

Аннотация

Artificial intelligence and machine learning have revolutionized the pharmaceutical industry, offering new approaches to drug discovery and development. These techniques have the potential to improve the efficiency and accuracy of the drug discovery process, leading to the development of more effective medications.In particular, AI-based algorithms can be employed to predict the efficacy and toxicity of new drug compounds, as well as to identify new targets for drug development. This paper provides an overview of the current landscape of AI in large-molecule drug discovery, highlighting the increasing application of these techniques to areas such as antibodies, gene therapies, and RNA-based therapies. The paper also discusses the challenges and opportunities associated with the use of AI in pharmaceutical research and development, emphasizing the importance of balancing the promise of AI with a continued reliance on the scientific method. While the promise of AI in pharmaceutical research is significant, it is crucial to recognize the limitations of these technologies and to maintain a balanced approach that leverages the strengths of both AI-driven and traditional, scientific methods. By doing so, researchers and developers can harness the power of AI to accelerate the drug discovery process, while ensuring that the development of new drugs remains grounded in robust scientific principles. Keywords: Artificial Intelligence, Machine Learning, Drug Discovery, Large Molecule Therapies, Pharmaceutical Research and Development. well-studied protein that is associated with a particular Introduction: The Utilizing Artificial Intelligence for disease. (Freedman, 2019) ,Predicting Binding Drug Prediction The pharmaceutical industry has long Interactions with AI-Bind One such AI-based tool is AI- relied on traditional methods of drug discovery, which Bind, which offers a powerful high-throughput are often time-consuming and inefficient. However, the approach to identify drug-target combinations. The rise of powerful statistical and biophysical modeling accurate prediction of binding interactions between programs, as well as the growth of the field of chemicals and proteins is a critical step in drug bioinformatics, has led to the development of discovery, as it helps to identify new drugs and novel computational tools that can predict the properties of therapeutic targets, reduce the failure rate in clinical molecules with greater accuracy and efficiency. trials, and predict the safety of drugs. (Chatterjee et al., (Freedman, 2019) Artificial intelligence techniques, 2021),AI-Bind utilizes machine learning algorithms to such as machine learning, are transforming the drug predict protein-ligand binding interactions, and the research and development process, enabled by the predictions are validated through docking simulations increasing availability of data and computational and comparison with recent experimental evidence. power. (Nagra et al., 2023),These AI-based approaches The tool also helps to identify potential active binding have the ability to improve the efficiency and accuracy sites on the amino acid sequence, providing valuable of drug discovery processes, leading to the insights into the interpretation of machine learning development of more effective medications. (Blanco- predictions of protein-ligand binding,. (Chatterjee et González et al., 2022) Some AI companies are focusing al., 2021),Expanding the Scope of Drug Discovery with on the problem of designing a drug that can safely and AI While traditional methods of pharmaceutical effectively work on a known target, usually a specific, American Journal of Applied Science and Technology 9 https://theusajournals.com/index.php/ajast American Journal of Applied Science and Technology (ISSN: 2771-2745) research have been relatively successful in the past, of AI-driven drug discovery is its ability to accurately they are limited by their reliance on trial-and-error predict binding interactions between chemicals and experimentation and their inability to accurately proteins, a critical step in identifying new drugs and predict the behavior of new potential bioactive therapeutic targets. (Chatterjee et al., 2021) Tools like compounds,. (Blanco-González et al., 2022) AI-based AI-Bind utilize machine learning algorithms to predict approaches, on the other hand, have the ability to protein-ligand binding, and validate these predictions expand the scope of drug discovery beyond the through docking simulations and experimental limitations of more conventional approaches, evidence. (Chatterjee et al., 2021) ,Beyond binding potentially leading to the development of novel and prediction, AI-based approaches also have the more effective medications (Blanco-González et al., potential to expand the scope of drug discovery. 2022). Traditional pharmaceutical research methods are often limited by their reliance on trial-and-error AI-Driven Approach to Identifying Appropriate