Deep learning based molecule generation for developing novel therapeutics for neurodegenerative diseases

Abstract

Deep learning models and generative AI have revolutionized drug discovery, especially with regard to neurodegenerative diseases like Alzheimer's. The developing of canonical SMILES (Simplified Molecular Input Line Entry System) for certain targets, such as BACE (Beta-secretase),a crucial enzyme linked to the disease progression of neurodegenerative disease, is a noteworthy application of these technologies. These models make use of large datasets to identify complex patterns in chemical structures, which allows them to synthesize new compounds with the necessary properties. The trick with BACE is to design compounds that can block its action only, without causing unwanted side effects.Driven by advanced deep learning architectures like transformers or bidirectional recurrent neural networks (RNNs), generative AI generates and refines SMILES strings iteratively until they meet predetermined standards for drug-like qualities, selectivity, and efficacy. This method drastically shortens the time and cost required for experimental synthesis and assessment, which speeds up the drug discovery process. These AI-driven approaches also make it easier to explore over large chemical landscapes, which may reveal new treatment prospects that traditional approaches might miss.As generative AI is iterative, researchers may gradually refine and enhance the quality of the compounds that are produced. Through the integration of feedback derived from computational assessments and experimental data, the model is capable of improving upon its errors and producing BACE inhibitors that are more potent and the compounds that are produced are docked against the BACE molecule to calculate the biding affinity,how effectively the molecule can bind with the BACE molecule. Iterative processes reduce the need for expensive and time-consuming laboratory studies while accelerating the identification of promising drugs candidates.Apart from producing new compounds, generative AI can also be applied to refine lead compounds that already exist for improved BACE inhibition. Through iterative modifications of the molecules based on feedback from experiments and computer predictions, researchers can optimize the attributes of lead compounds to enhance their safety, selectivity, and efficacy. All things considered, deep learning models and generative AI have great potential to advance drug discovery for neurodegenerative illnesses. These technologies enable researchers to find Alzheimer's and other debilitating disorders' cures more quickly by fusing complex algorithms with computational capacity. There is growing optimism over the development of transformational therapeutics for neurodegenerative disorders as ongoing research continues to hone and enhance the capabilities of generative AI and deep learning models.