Origin of aromatase inhibitory activity via proteochemometric modeling

DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists

Drug resistance represents a major obstacle to therapeutic innovations and is a prevalent feature in prostate cancer (PCa). Androgen receptors (ARs) are the hallmark therapeutic target for prostate cancer modulation and AR antagonists have achieved great success. However, rapid emergence of resistance contributing to PCa progression is the ultimate burden of their long-term usage. Hence, the discovery and development of AR antagonists with capability to combat the resistance, remains an avenue for further exploration. Therefore, this study proposes a novel deep learning (DL)-based hybrid framework, named DeepAR, to accurately and rapidly identify AR antagonists by using only the SMILES notation. Specifically, DeepAR is capable of extracting and learning the key information embedded in AR antagonists. Firstly, we established a benchmark dataset by collecting active and inactive compounds against AR from the ChEMBL database. Based on this dataset, we developed and optimized a collection of baseline models by using a comprehensive set of well-known molecular descriptors and machine learning algorithms. Then, these baseline models were utilized for creating probabilistic features. Finally, these probabilistic features were combined and used for the construction of a meta-model based on a one-dimensional convolutional neural network. Experimental results indicated that DeepAR is a more accurate and stable approach for identifying AR antagonists in terms of the independent test dataset, by achieving an accuracy of 0.911 and MCC of 0.823. In addition, our proposed framework is able to provide feature importance information by leveraging a popular computational approach, named SHapley Additive exPlanations (SHAP). In the meanwhile, the characterization and analysis of potential AR antagonist candidates were achieved through the SHAP waterfall plot and molecular docking. The analysis inferred that N-heterocyclic moieties, halogenated substituents, and a cyano functional group were significant determinants of potential AR antagonists. Lastly, we implemented an online web server by using DeepAR (at http://pmlabstack.pythonanywhere.com/DeepAR ). We anticipate that DeepAR could be a useful computational tool for community-wide facilitation of AR candidates from a large number of uncharacterized compounds.

Pathophysiological Effects of Contemporary Lifestyle on Evolutionary-Conserved Survival Mechanisms in Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) is increasingly being characterized as an evolutionary mismatch disorder that presents with a complex mixture of metabolic and endocrine symptoms. The Evolutionary Model proposes that PCOS arises from a collection of inherited polymorphisms that have been consistently demonstrated in a variety of ethnic groups and races. In utero developmental programming of susceptible genomic variants are thought to predispose the offspring to develop PCOS. Postnatal exposure to lifestyle and environmental risk factors results in epigenetic activation of developmentally programmed genes and disturbance of the hallmarks of health. The resulting pathophysiological changes represent the consequences of poor-quality diet, sedentary behaviour, endocrine disrupting chemicals, stress, circadian disruption, and other lifestyle factors. Emerging evidence suggests that lifestyle-induced gastrointestinal dysbiosis plays a central role in the pathogenesis of PCOS. Lifestyle and environmental exposures initiate changes that result in disturbance of the gastrointestinal microbiome (dysbiosis), immune dysregulation (chronic inflammation), altered metabolism (insulin resistance), endocrine and reproductive imbalance (hyperandrogenism), and central nervous system dysfunction (neuroendocrine and autonomic nervous system). PCOS can be a progressive metabolic condition that leads to obesity, gestational diabetes, type two diabetes, metabolic-associated fatty liver disease, metabolic syndrome, cardiovascular disease, and cancer. This review explores the mechanisms that underpin the evolutionary mismatch between ancient survival pathways and contemporary lifestyle factors involved in the pathogenesis and pathophysiology of PCOS.

Prediction of KRASG12C inhibitors using conjoint fingerprint and machine learning-based QSAR models

Kirsten rat sarcoma virus G12C (KRAS^G12C) is the major protein mutation associated with non-small cell lung cancer (NSCLC) severity. Inhibiting KRAS^G12C is therefore one of the key therapeutic strategies for NSCLC patients. In this paper, a cost-effective data driven drug design employing machine learning-based quantitative structure-activity relationship (QSAR) analysis was built for predicting ligand affinities against KRAS^G12C protein. A curated and non-redundant dataset of 1033 compounds with KRAS^G12C inhibitory activity (pIC₅₀) was used to build and test the models. The PubChem fingerprint, Substructure fingerprint, Substructure fingerprint count, and the conjoint fingerprint-a combination of PubChem fingerprint and Substructure fingerprint count-were used to train the models. Using comprehensive validation methods and various machine learning algorithms, the results clearly showed that the XGBoost regression (XGBoost) achieved the highest performance in term of goodness of fit, predictivity, generalizability and model robustness (R² = 0.81, Q²_CV = 0.60, Q²_Ext = 0.62, R² - Q²_Ext = 0.19, R²_Y-Random = 0.31 ± 0.03, Q²_Y-Random = -0.09 ± 0.04). The top 13 molecular fingerprints that correlated with the predicted pIC₅₀ values were SubFPC274 (aromatic atoms), SubFPC307 (number of chiral-centers), PubChemFP37 (≥1 Chlorine), SubFPC18 (Number of alkylarylethers), SubFPC1 (number of primary carbons), SubFPC300 (number of 1,3-tautomerizables), PubChemFP621 (N-C:C:C:N structure), PubChemFP23 (≥1 Fluorine), SubFPC2 (number of secondary carbons), SubFPC295 (number of C-ONS bonds), PubChemFP199 (≥4 6-membered rings), PubChemFP180 (≥1 nitrogen-containing 6-membered ring), and SubFPC180 (number of tertiary amine). These molecular fingerprints were virtualized and validated using molecular docking experiments. In conclusion, this conjoint fingerprint and XGBoost-QSAR model demonstrated to be useful as a high-throughput screening tool for KRAS^G12C inhibitor identification and drug design.

Proteochemometrics - recent developments in bioactivity and selectivity modeling

Proteochemometrics is a machine learning based modeling approach relying on a combination of ligand and protein descriptors. With ongoing developments in machine learning and increases in public data the technique is more frequently applied in early drug discovery, typically in ligand-target binding prediction. Common applications include improvements to single target quantitative structure-activity relationship models, protein selectivity and promiscuity modeling, and large-scale deep learning approaches. The increase in predictive power using proteochemometrics is observed in multi-target bioactivity modeling, opening the door to more extensive studies covering whole protein families. On top of that, with deep learning fueling more complex and larger scale models, proteochemometrics allows faster and higher quality computational models supporting the design, make, test cycle.

New insights into the selective inhibition of the β-carbonic anhydrases of pathogenic bacteria Burkholderia pseudomallei and Francisella tularensis: a proteochemometrics study

Nowadays, antibiotic resistance has turned into one of the most important worldwide health problems. Biological end point of critical enzymes induced by potent inhibitors is recently being considered as a highly effective and popular strategy to defeat antibiotic-resistant pathogens. For instance, the simple but critical β-carbonic anhydrase has recently been in the center of attention for anti-pathogen drug discoveries. However, no β-carbonic anhydrase selective inhibitor has yet been developed. Available β-carbonic anhydrase inhibitors are also highly potent with regard to human carbonic anhydrases, leading to severe inevitable side effects in case of usage. Therefore, developing novel inhibitors with high selectivity against pathogenic β-carbonic anhydrases is of great essence. Herein, for the first time, we have conducted a proteochemometric study to explore the structural and the chemical aspects of the interactions governed by bacterial β-carbonic anhydrases and their inhibitors. We have found valuable information which can lead to designing novel inhibitors with better selectivity for bacterial β-carbonic anhydrases.

Towards understanding aromatase inhibitory activity via QSAR modeling

Aromatase is a rate-limiting enzyme for estrogen biosynthesis that is overproduced in breast cancer tissue. To block the growth of breast tumors, aromatase inhibitors (AIs) are employed to bind and inhibit aromatase in order to lower the amount of estrogen produced in the body. Although a number of synthetic aromatase inhibitors have been released for clinical use in the treatment of hormone-receptor positive breast cancer, these inhibitors may lead to undesirable side effects (e.g. increased rash, diarrhea and vomiting; effects on the bone, brain and heart) and therefore, the search for novel AIs continues. Over the past decades, there has been an intense effort in employing medicinal chemistry and quantitative structure-activity relationship (QSAR) to shed light on the mechanistic basis of aromatase inhibition. To the best of our knowledge, this article constitutes the first comprehensive review of all QSAR studies of both steroidal and non-steroidal AIs that have been published in the field. Herein, we summarize the experimental setup of these studies as well as summarizing the key features that are pertinent for robust aromatase inhibition.

Probing the chemical interaction space governed by 4-aminosubstituted benzenesulfonamides and carbonic anhydrase isoforms

Isoform diversity, critical physiological roles and involvement in major diseases/disorders such as glaucoma, epilepsy, Alzheimer's disease, obesity, and cancers have made carbonic anhydrase (CA), one of the most interesting case studies in the field of computer aided drug design. Since applying non-selective inhibitors can result in major side effects, there have been considerable efforts so far to achieve selective inhibitors for different isoforms of CA. Using proteochemometrics approach, the chemical interaction space governed by a group of 4-amino-substituted benzenesulfonamides and human CAs has been explored in the present study. Several validation methods have been utilized to assess the validity, robustness and predictivity power of the proposed proteochemometric model. Our model has offered major structural information that can be applied to design new selective inhibitors for distinct isoforms of CA. To prove the applicability of the proposed model, new compounds have been designed based on the offered discriminative structural features.

3D proteochemometrics: using three-dimensional information of proteins and ligands to address aspects of the selectivity of serine proteases

The high similarity between certain sub-pockets of serine proteases may lead to low selectivity of protease inhibitors. Therefore the application of proteochemometrics (PCM), which quantifies the relationship between protein/ligand descriptors and affinity for multiple ligands and targets simultaneously, is useful to understand and improve the selectivity profiles of potential inhibitors. In this study, protein field-based PCM that uses knowledge-based and WaterMap derived fields to describe proteins in combination with 2D (RDKit and MOE fingerprints) and 3D (4 point pharmacophoric fingerprints and GRIND) ligand descriptors was used to model the bioactivities of 24 homologous serine proteases and 5863 inhibitors in an integrated fashion. Of the multiple field-based PCM models generated based on different ligand descriptors, RDKit fingerprints showed the best performance in terms of external prediction with Rtest2 of 0.72 and RMSEP of 0.81. Further, visual interpretation of the models highlights sub-pocket specific regions that influence affinity and selectivity of serine protease inhibitors.

Exploring the origin of phosphodiesterase inhibition via proteochemometric modeling

A proteochemometric study of a set of phosphodiesterase 4B and 4D inhibitors sheds light on the origin of their inhibition and selectivities.