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Accelerating Drug Discovery and Opti...

Bhattarai, Nirajan,

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Accelerating Drug Discovery and Optimization With Artificial Intelligence: Applications in Nicotinic System Modulation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Accelerating Drug Discovery and Optimization With Artificial Intelligence: Applications in Nicotinic System Modulation // Nirajan Bhattarai.
作者:	Bhattarai, Nirajan,
面頁冊數:	1 electronic resource (270 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
Contained By:	Dissertations Abstracts International86-07B.
標題:	Pharmaceutical sciences. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31634224
ISBN:	9798302827715

Accelerating Drug Discovery and Optimization With Artificial Intelligence: Applications in Nicotinic System Modulation /
Bhattarai, Nirajan,

Accelerating Drug Discovery and Optimization With Artificial Intelligence: Applications in Nicotinic System Modulation /Nirajan Bhattarai. - 1 electronic resource (270 pages)

Source: Dissertations Abstracts International, Volume: 86-07, Section: B.

The primary objective of the dissertation is the discovery of novel Acetylcholinesterase (AChE) inhibitors and Positive Allosteric Modulators of Nicotinic Acetylcholine Receptors (nAChR) using artificial intelligence (AI)-driven methodologies to address disorders related to synaptic nicotinic system dysfunction. The study begins with a Structure-Activity Relationship (SAR) study on Galantamine, a known AChE inhibitor, to identify structural requirements for inhibiting AChE and positive allosteric modulation of α7 nAChR. Ten analogs, generated with the systematic structural deconstruction of Galantamine, were synthesized and evaluated for their activity on both receptors, highlighting cyclohexa(e)nyl ring and/or hydroxyl group as optimal for activity. In the second part, we developed comprehensive, well-validated, interpretable, and accessible machine-learning tools and platforms for predicting or identifying novel small-molecule AChE inhibitors. The high performance of the best-derived models, which were derived from different model types within each variant type, was observed and further validated using cross-species datasets. Key structural components were identified through model interpretations, and important AChE inhibitors were discovered by screening large compound datasets. These findings were further validated with docking studies on selected compounds intended for experimental validation. Finally, the models and the model development and deployment process were made accessible through a user-friendly Streamlit web application. The third part, AI-driven inverse design approaches, incorporating Tree-based Pipeline Optimization Tool Automated Machine Learning (AutoML TPOT) and Local Interpretable Model-agnostic Explanations (LIME), for the generation of new potential compounds with desired pharmacological properties, and chemical data augmentation, was presented. The emphasis of the dissertation is that the synergy of AI tools and traditional drug discovery methods has the potential to accelerate the identification of novel therapeutics, especially modulators of the nicotinic acetylcholine system. Finally, challenges in AI-enhanced drug discovery of nAChR PAMs, due to limited data, were presented, and the proposed solutions include novel approaches developed in the realm of AI drug discovery, such as chemical data augmentation, few-shot learning, and transfer learning, in combination with traditional computational approaches like docking studies and pharmacophore modeling.

English

ISBN: 9798302827715Subjects--Topical Terms:

3173021
Pharmaceutical sciences.
Subjects--Index Terms:

Acetylcholinesterase

Accelerating Drug Discovery and Optimization With Artificial Intelligence: Applications in Nicotinic System Modulation /
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520 $a The primary objective of the dissertation is the discovery of novel Acetylcholinesterase (AChE) inhibitors and Positive Allosteric Modulators of Nicotinic Acetylcholine Receptors (nAChR) using artificial intelligence (AI)-driven methodologies to address disorders related to synaptic nicotinic system dysfunction. The study begins with a Structure-Activity Relationship (SAR) study on Galantamine, a known AChE inhibitor, to identify structural requirements for inhibiting AChE and positive allosteric modulation of α7 nAChR. Ten analogs, generated with the systematic structural deconstruction of Galantamine, were synthesized and evaluated for their activity on both receptors, highlighting cyclohexa(e)nyl ring and/or hydroxyl group as optimal for activity. In the second part, we developed comprehensive, well-validated, interpretable, and accessible machine-learning tools and platforms for predicting or identifying novel small-molecule AChE inhibitors. The high performance of the best-derived models, which were derived from different model types within each variant type, was observed and further validated using cross-species datasets. Key structural components were identified through model interpretations, and important AChE inhibitors were discovered by screening large compound datasets. These findings were further validated with docking studies on selected compounds intended for experimental validation. Finally, the models and the model development and deployment process were made accessible through a user-friendly Streamlit web application. The third part, AI-driven inverse design approaches, incorporating Tree-based Pipeline Optimization Tool Automated Machine Learning (AutoML TPOT) and Local Interpretable Model-agnostic Explanations (LIME), for the generation of new potential compounds with desired pharmacological properties, and chemical data augmentation, was presented. The emphasis of the dissertation is that the synergy of AI tools and traditional drug discovery methods has the potential to accelerate the identification of novel therapeutics, especially modulators of the nicotinic acetylcholine system. Finally, challenges in AI-enhanced drug discovery of nAChR PAMs, due to limited data, were presented, and the proposed solutions include novel approaches developed in the realm of AI drug discovery, such as chemical data augmentation, few-shot learning, and transfer learning, in combination with traditional computational approaches like docking studies and pharmacophore modeling.
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