Artificial intelligence and machine learning based discovery of bioisosteres (BIOS) of FDA approved MAO-B inhibitors for the management of Alzheimer’s disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder categorized by cognitive decline, synaptic dysfunction and complex multifactorial pathology. Among its molecular contributors, monoamine oxidase-B (MAO-B) plays a significant role in oxidative stress and neurotransmitter imbalance. Given the limited efficacy, off-target risks and pharmacokinetic constraints of existing MAO-B inhibitors such as safinamide (SAF), this study aimed to design improved analogues through AI-driven bioisosteric optimization. Using five FDA-approved MAO-B inhibitors as chemical templates, a total of 14,552 Bioisosteres (BIOS) were generated via the MolOpt AI platform. These structures were refined through Lipinski filters and TOPKAT toxicity models, yielding 440 viable candidates. Their inhibitory potential was assessed using an ML-based MAO-B predictor employing PubChem fingerprints, substructure keys and 1D/2D descriptors, with all BIOS displaying predicted nanomolar (nM) activity. Docking simulations against MAO-B (PDB ID: 2V5Z) using the CDOCKER algorithm identified ten high-affinity hits with superior interaction energies and enriched residue interactions compared to SAF. Subsequent ADMET profiling through ADMETlab 3.0 prioritized two leads BIOS 5261 and BIOS 7673 exhibiting favourable pharmacokinetics, low predicted toxicity and enhanced blood brain barrier permeability. Complementary AI-based target prediction (MolPredictX) indicated strong affinity toward NADPH and COX-2, suggesting potential multitarget relevance in AD modulation. Collectively, this integrated AI/ML-guided framework enabled the efficient discovery of structurally novel, drug-like MAO-B inhibitors with improved computational profiles. The findings underscore the power of bioisosteric design combined with computational intelligence for developing next-generation therapeutic candidates for AD.

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Publication Details

Journal
Discover Chemistry.
Published
2026-04-01
DOI
https://doi.org/10.1007/s44371-026-00634-4
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

Artificial intelligence and machine learning based discovery of bioisosteres (BIOS) of FDA approved MAO-B inhibitors for the management of Alzheimer’s disease

Balaji Wamanrao Matore, Nikita Chhabra, Anjali Murmu, Jagadish Singh et al.
Discover Chemistry.
Computational Drug Discovery Methods
article

Artificial intelligence and machine learning based discovery of bioisosteres (BIOS) of FDA approved MAO-B inhibitors for the management of Alzheimer’s disease

Balaji Wamanrao Matore, Nikita Chhabra, Anjali Murmu, Jagadish Singh, Sanjana Jaiswal, Shaili Singh Parmar, Pragya Gawande, Partha Pratim Roy, Akshay Kumar, Krishna Kumar
article en

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder categorized by cognitive decline, synaptic dysfunction and complex multifactorial pathology. Among its molecular contributors, monoamine oxidase-B (MAO-B) plays a significant role in oxidative stress and neurotransmitter imbalance. Given the limited efficacy, off-target risks and pharmacokinetic constraints of existing MAO-B inhibitors such as safinamide (SAF), this study aimed to design improved analogues through AI-driven bioisosteric optimization. Using five FDA-approved MAO-B inhibitors as chemical templates, a total of 14,552 Bioisosteres (BIOS) were generated via the MolOpt AI platform. These structures were refined through Lipinski filters and TOPKAT toxicity models, yielding 440 viable candidates. Their inhibitory potential was assessed using an ML-based MAO-B predictor employing PubChem fingerprints, substructure keys and 1D/2D descriptors, with all BIOS displaying predicted nanomolar (nM) activity. Docking simulations against MAO-B (PDB ID: 2V5Z) using the CDOCKER algorithm identified ten high-affinity hits with superior interaction energies and enriched residue interactions compared to SAF. Subsequent ADMET profiling through ADMETlab 3.0 prioritized two leads BIOS 5261 and BIOS 7673 exhibiting favourable pharmacokinetics, low predicted toxicity and enhanced blood brain barrier permeability. Complementary AI-based target prediction (MolPredictX) indicated strong affinity toward NADPH and COX-2, suggesting potential multitarget relevance in AD modulation. Collectively, this integrated AI/ML-guided framework enabled the efficient discovery of structurally novel, drug-like MAO-B inhibitors with improved computational profiles. The findings underscore the power of bioisosteric design combined with computational intelligence for developing next-generation therapeutic candidates for AD.

Discover Chemistry.Vol. 3(1)
Haldia Institute of Technology (IN), Guru Ghasidas Vishwavidyalaya (IN)
Openalex Percentile: Top 30%
Computational Drug Discovery Methods
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