An integrated computer-aided drug design (CADD) and artificial intelligence (AI) framework to systematically identify selective ALDH1A1 inhibitors from a heterocyclic compound library is developed and suggests that LDN-27219 exhibits favorable binding characteristics and represents a promising lead candidate for subsequent experimental validation.
GSK3BMTPred, a multitask deep neural network model, was developed for simultaneous prediction of inhibitor classification and inhibitory potency and identified compounds showing stable interactions with key Adenosine Triphosphate (ATP) residues and favorable predicted absorption, distribution, metabolism, excretion, an...
Findings highlight Z4P as a promising mutation-resilient IRE1 inhibitor and validate the effectiveness of the integrated computational pipeline for identifying potential anti-cancer therapeutics.
Nithisha L Bastin, P. K. Praveen Kumar, B. Ethiraj et al.· Scientific Reports· 0 citations
The proposed workflow efficiently reduced a large chemical space to a focused set of TNKS1 inhibitor candidates while substantially reducing the experimental screening burden, highlighting the value of integrating consensus ML, SBVS, and experimental validation to accelerate early-stage hit discovery for TNKS1 and othe...
M. Bilotta, Adriana Gargano, R. Rocca et al.· Pharmaceuticals· 0 citations
An integrated computational framework combining machine learning (ML), deep learning (DL), and structure-based docking with experimental validation identifies AO65 as a promising lead for further TDP1-focused investigation.
Huang Zeng, Man-Yi Zhang, Bo Qiu et al.· RSC Advances· 0 citations
These findings introduce H_1 as a computationally prioritized, putative MLK4-binding lead and provide a hypothesis-generating framework for MLK4-targeted scaffold prioritization, while recognizing that experimental activity and kinome selectivity profiling remain necessary before H_1 can be described as a confirmed MLK...
Afnan A. Alzaghari, S. Daoud, Husam Nassar et al.· Journal of Pharmaceutical In...· 0 citations
The rapid emergence of metallo-b-lactamase-mediated antibiotic resistance has created an urgent need for new inhibitor discovery strategies. In this work, a machine-learning-guided workflow was developed to generate and prioritize potential inhibitors targeting NDM-1. A SMILES-based variational autoencoder was first pr...
Anthony M. Baudino, Kari L. Stone· AI Chemistry· 0 citations
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