Identification of novel monoamine oxidase selective inhibitors employing a hierarchical ligand-based virtual screening strategy

Identification of Riluzole derivatives as novel calmodulin inhibitors with neuroprotective activity by a joint synthesis, biosensor, and computational guided strategy

The development of new molecules for the treatment of calmodulin related cardiovascular or neurodegenerative diseases is an interesting goal. In this work, we introduce a novel strategy with four main steps: (1) chemical synthesis of target molecules, (2) Förster Resonance Energy Transfer (FRET) biosensor development and in vitro biological assay of new derivatives, (3) Cheminformatics models development and in vivo activity prediction, and (4) Docking studies. This strategy is illustrated with a case study. Firstly, a series of 4-substituted Riluzole derivatives 1-3 were synthetized through a strategy that involves the construction of the 4-bromoriluzole framework and its further functionalization via palladium catalysis or organolithium chemistry. Next, a FRET biosensor for monitoring Ca2+-dependent CaM-ligands interactions has been developed and used for the in vitro assay of Riluzole derivatives. In particular, the best inhibition (80%) was observed for 4-methoxyphenylriluzole 2b. Besides, we trained and validated a new Networks Invariant, Information Fusion, Perturbation Theory, and Machine Learning (NIFPTML) model for predicting probability profiles of in vivo biological activity parameters in different regions of the brain. Next, we used this model to predict the in vivo activity of the compounds experimentally studied in vitro. Last, docking study conducted on Riluzole and its derivatives has provided valuable insights into their binding conformations with the target protein, involving calmodulin and the SK4 channel. This new combined strategy may be useful to reduce assay costs (animals, materials, time, and human resources) in the drug discovery process of calmodulin inhibitors.

Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors

Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors

Machine learning including modern deep learning models has been extensively used in drug design and screening. However, reliable prediction of molecular properties is still challenging when exploring out-of-domain regimes, even for deep neural networks. Therefore, it is important to understand the uncertainty of model predictions, especially when the predictions are used to guide further experiments. In this study, we explored the utility and effectiveness of evidential uncertainty in compound screening. The evidential Graphormer model was proposed for uncertainty-guided discovery of KDM1A/LSD1 inhibitors. The benchmarking results illustrated that (i) Graphormer exhibited comparative predictive power to state-of-the-art models, and (ii) evidential regression enabled well-ranked uncertainty estimates and calibrated predictions. Subsequently, we leveraged time-splitting on the curated KDM1A/LSD1 dataset to simulate out-of-distribution predictions. The retrospective virtual screening showed that the evidential uncertainties helped reduce false positives among the top-acquired compounds and thus enabled higher experimental validation rates. The trained model was then used to virtually screen an independent in-house compound set. The top 50 compounds ranked by two different ranking strategies were experimentally validated, respectively. In general, our study highlighted the importance to understand the uncertainty in prediction, which can be recognized as an interpretable dimension to model predictions.

Identification of new small molecule monoamine oxidase-B inhibitors through pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation studies

The discovery of a safe and efficacious drug is a complex, time-consuming, and expensive process. Computational methodologies driven by cheminformatics tools play a central role in the high-throughput lead discovery and optimization process especially when the structure of the biological target is known. Monoamine oxidases are the membrane-bound FAD-containing enzymes and the isoform monoamine oxidase-B (MAO-B) is an attractive target for treating diseases like Alzheimer's disease, Parkinson's disease, glioma, etc. In the current study, we have used a pharmacophore-based virtual screening technique for the identification of new small molecule MAO-B inhibitors. Safinamide was used for building a pharmacophore model and the developed model was used to probe the ZINC database for potential hits. The obtained hits were filtered against drug-likeness and PAINS. Out of the hit's library, two compounds ZINC02181408, ZINC08853942 (most active), and ZINC53327382 (least active) were further subjected to molecular docking and dynamics simulation studies to assess their virtual binding affinities and stability of the resultant protein-ligand complex. The docking studies revealed that active ligands were well accommodated within the active site of MAO-B and interacted with both substrate and entrance cavity residues. MD simulation studies unveiled additional hydrogen bond interactions with the substrate cavity residues, Tyr398 and Tyr435 that are crucial for the catalytic role of MAO-B. Moreover, the predicted ADMET parameters suggest that the compounds ZINC08853942 and ZINC02181408 are suitable for CNS penetration. Thus, the attempted computational campaign yielded two potential MAO-B inhibitors that merit further experimental investigation.Communicated by Ramaswamy H. Sarma.

Accelerating the identification of subtype selective inhibitors via Three-Dimensional Biologically Relevant Spectrum (BRS-3D): The monoamine oxidase subtypes as a case study

Subtype-selective drugs are of great therapeutic importance as they are expected to be more effective and with less side-effects. However, discovery of subtype selective inhibitors was hampered by the high similarity of the binding sites within subfamilies. In this study, we further evaluated the applicability of "Three-Dimensional Biologically Relevant Spectrum (BRS-3D)" for the identification of subtype-selective inhibitors. A case study was performed on monoamine oxidase, which has two subtypes related to distinct diseases. The inhibitory activity against MAO-A/B of 347 compounds experimentally tested in this research was reported. Compound M124 (5H-thiazolo[3,2-a]pyrimidin-5-one) with IC₅₀ less than 100 nM (SI = 23) was selected as a probe to investigate the structure selectivity relationship. Similarity search led to the identification of compound M229 and M249 with IC₅₀ values of 7.4 nM, 4 nM and acceptable selectivity index over MAO-A (M229 SI > 1351, M249 SI > 2500). The molecular basis for subtype selectivity was explored through docking study and attention based DNN model. Additionally, in silico ADME properties were characterized. Accordingly, it is found that BRS-3D is a robust method for subtype selectivity in the early stage of drug discovery and the compounds reported here can be promising leads for further experimental analysis.

Novel C7-Substituted Coumarins as Selective Monoamine Oxidase Inhibitors: Discovery, Synthesis and Theoretical Simulation

There is a continued need to develop new selective human monoamine oxidase (hMAO) inhibitors that could be beneficial for the treatment of neurological diseases. However, hMAOs are closely related with high sequence identity and structural similarity, which hinders the development of selective MAO inhibitors. “Three-Dimensional Biologically Relevant Spectrum (BRS-3D)” method developed by our group has demonstrated its effectiveness in subtype selectivity studies of receptor and enzyme ligands. Here, we report a series of novel C7-substituted coumarins, either synthesized or commercially purchased, which were identified as selective hMAO inhibitors. Most of the compounds demonstrated strong activities with IC₅₀ values (half-inhibitory concentration) ranging from sub-micromolar to nanomolar. Compounds, FR1 and SP1, were identified as the most selective hMAO-A inhibitors, with IC₅₀ values of 1.5 nM (selectivity index (SI) < −2.82) and 19 nM (SI < −2.42), respectively. FR4 and FR5 showed the most potent hMAO-B inhibitory activity, with IC₅₀ of 18 nM and 15 nM (SI > 2.74 and SI > 2.82). Docking calculations and molecular dynamic simulations were performed to elucidate the selectivity preference and SAR profiles.