Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer’s disease

Dhamodharan, G.; Mohan, C. Gopi

doi:10.1007/s11030-021-10282-8

Cited by 27 publications

(14 citation statements)

References 38 publications

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“…To date there have been several examples of other research groups using machine learning to create models for AChE inhibition, ,− including recent papers using ChEMBL or BindingDB as sources for their training sets. ,, Many of the studies used 3D-QSAR and a docking step for scoring. Our models in contrast use only ECFP6 fingerprints, which do not require 3D information on the target.…”

Section: Discussionmentioning

confidence: 99%

Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species

Vignaux

Lane

Urbina

et al. 2023

Chem. Res. Toxicol.

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Acetylcholinesterase (AChE) is an important enzyme and target for human therapeutics, environmental safety, and global food supply. Inhibitors of this enzyme are also used for pest elimination and can be misused for suicide or chemical warfare. Adverse effects of AChE pesticides on nontarget organisms, such as fish, amphibians, and humans, have also occurred as a result of biomagnifications of these toxic compounds. We have exhaustively curated the public data for AChE inhibition data and developed machine learning classification models for seven different species. Each set of models were built using up to nine different algorithms for each species and Morgan fingerprints (ECFP6) with an activity cutoff of 1 μM. The human (4075 compounds) and eel (5459 compounds) consensus models predicted AChE inhibition activity using external test sets from literature data with 81% and 82% accuracy, respectively, while the reciprocal cross (76% and 82% percent accuracy) was not species-specific. In addition, we also created machine learning regression models for human and eel AChE inhibition to return a predicted IC50 value for a queried molecule. We did observe an improved species specificity in the regression models, where a human support vector regression model of human AChE inhibition (3652 compounds) predicted the IC50s of the human test set to a better extent than the eel regression model (4930 compounds) on the same test set, based on mean absolute percentage error (MAPE = 9.73% vs 13.4%). The predictive power of these models certainly benefits from increasing the chemical diversity of the training set, as evidenced by expanding our human classification model by incorporating data from the Tox21 library of compounds. Of the 10 compounds we tested that were predicted active by this expanded model, two showed >80% inhibition at 100 μM. This machine learning approach therefore offers the ability to rapidly score massive libraries of molecules against the models for AChE inhibition that can then be selected for future in vitro testing to identify potential toxins. It also enabled us to create a public website, MegaAChE, for single-molecule predictions of AChE inhibition using these models at .

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Section: Discussionmentioning

confidence: 99%

Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species

Vignaux

Lane

Urbina

et al. 2023

Chem. Res. Toxicol.

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“…PC descriptors of the structures and molecular fingerprints are used in these techniques. 34 , 35 , 38 The potential for solving issues has been dramatically increased as a result. Using the methods of artificial neural network QSAR ANN , support vector regression QSAR SVR , and kernel-based PLS regression QSAR KPLS , models were created from the relation between molecular descriptors and activity.…”

Section: Introductionmentioning

confidence: 99%

“…With the development of supervised machine learning methods such as support vector regression, partial least-squares (PLS) regression, and artificial neural networks, these methods have now been significantly improved (SVR). PC descriptors of the structures and molecular fingerprints are used in these techniques. ,, The potential for solving issues has been dramatically increased as a result. Using the methods of artificial neural network QSAR ANN , support vector regression QSAR SVR , and kernel-based PLS regression QSAR KPLS , models were created from the relation between molecular descriptors and activity. ,, A trustworthy predictive QSAR model has been created that can be utilized to direct the creation of new chemicals and routine synthesis or acquisition of additional compounds.…”

Section: Introductionmentioning

confidence: 99%

Novel Thiosemicarbazone Quantum Dots in the Treatment of Alzheimer’s Disease Combining In Silico Models Using Fingerprints and Physicochemical Descriptors

et al. 2023

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Searching for thiosemicarbazone derivatives with the potential to inhibit acetylcholinesterase for the treatment of Alzheimer’s disease (AD) is an important current goal. The QSARKPLS, QSARANN, and QSARSVR models were constructed using binary fingerprints and physicochemical (PC) descriptors of 129 thiosemicarbazone compounds screened from a database of 3791 derivatives. The R 2 and Q 2 values for the QSARKPLS, QSARANN, and QSARSVR models are greater than 0.925 and 0.713 using dendritic fingerprint (DF) and PC descriptors, respectively. The in vitro pIC50 activities of four new design-oriented compounds N1, N2, N3, and N4, from the QSARKPLS model using DFs, are consistent with the experimental results and those from the QSARANN and QSARSVR models. The designed compounds N1, N2, N3, and N4 do not violate Lipinski-5 and Veber rules using the ADME and BoiLED-Egg methods. The binding energy, kcal mol–1, of the novel compounds to the 1ACJ-PDB protein receptor of the AChE enzyme was also obtained by molecular docking and dynamics simulations consistent with those predicted from the QSARANN and QSARSVR models. New compounds N1, N2, N3, and N4 were synthesized, and the experimental in vitro pIC50 activity was determined in agreement with those obtained from in silico models. The newly synthesized thiosemicarbazones N1, N2, N3, and N4 can inhibit 1ACJ-PDB, which is predicted to be able to cross the barrier. The DFT B3LYP/def-SV(P)-ECP quantization calculation method was used to calculate E HOMO and E LUMO to account for the activities of compounds N1, N2, N3, and N4. The quantum calculation results explained are consistent with those obtained in in silico models. The successful results here may contribute to the search for new drugs for the treatment of AD.

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“…In recent years, machine learning (ML) has become advantageous in assisting researchers to identify potential candidates for clinical trials. For instance, novel compounds against Alzheimer, COVID-19, Candida albicans infection and cancer were identified by ML based virtual screening strategy [ 10 – 13 ]. In another instance, inhibitors against cytochrome P450, LasR, DDP-4, EGFR, HSP 90 , FXa and CDK 2 were successfully identified by scrutinizing large databases using ML and docking techniques [ 14 – 17 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer

2022

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Non-small cell lung cancer (NSCLC) remains the leading cause of mortality and morbidity worldwide accounting about 85% of total lung cancer cases. The receptor REarranged during Transfection (RET) plays an important role by ligand independent activation of kinase domain resulting in carcinogenesis. Presently, the treatment for RET driven NSCLC is limited to multiple kinase inhibitors. This situation necessitates the discovery of novel and potent RET specific inhibitors. Thus, we employed high throughput screening strategy to repurpose FDA approved compounds from DrugBank comprising of 2509 molecules. It is worth noting that the initial screening is accomplished with the aid of in-house machine learning model built using IC 50 values corresponding to 2854 compounds obtained from BindingDB repository. A total of 497 compounds (19%) were predicted as actives by our generated model. Subsequent in silico validation process such as molecular docking, MMGBSA and density function theory analysis resulted in identification of two lead compounds named DB09313 and DB00471. The simulation study highlights the potency of DB00471 (Montelukast) as potential RET inhibitor among the investigated compounds. In the end, the half-minimal inhibitory activity of montelukast was also predicted against RET protein expressing LC-2/ad cell lines demonstrated significant anticancer activity. Collective analysis from our study highlights that montelukast could be a promising candidate for the management of RET specific NSCLC. Supplementary Information The online version contains supplementary material available at 10.1007/s12032-022-01924-4.

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Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer’s disease

Cited by 27 publications

References 38 publications

Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species

Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species

Novel Thiosemicarbazone Quantum Dots in the Treatment of Alzheimer’s Disease Combining In Silico Models Using Fingerprints and Physicochemical Descriptors

Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer

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