Discovery of Dual FGFR4 and EGFR Inhibitors by Machine Learning and Biological Evaluation

From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors

Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effects. Thus, finding novel and more effective inhibitors is of utmost importance. In this study, a deep learning (DL) classification model was first developed and then employed to select putative active VEGFR-2 inhibitors from an in-house chemical library including 187 druglike compounds. A pool of 18 promising candidates was shortlisted and screened against VEGFR-2 by using molecular docking. Finally, two compounds, RHE-334 and EA-11, were prioritized as promising VEGFR-2 inhibitors by employing PLATO, our target fishing and bioactivity prediction platform. Based on this rationale, we prepared RHE-334 and EA-11 and successfully tested their anti-proliferative potential against MCF-7 human breast cancer cells with IC50 values of 26.78±4.02 and 38.73±3.84 μM, respectively. Their toxicities were instead challenged against the WI-38. Interestingly, expression studies indicated that, in the presence of RHE-334, VEGFR-2 was equal to 0.52±0.03, thus comparable to imatinib equal to 0.63±0.03. In conclusion, this workflow based on theoretical and experimental approaches demonstrates effective in identifying VEGFR-2 inhibitors and can be easily adapted to other medicinal chemistry goals.

Discovery of novel ULK1 inhibitors through machine learning-guided virtual screening and biological evaluation

Aim: Build a virtual screening model for ULK1 inhibitors based on artificial intelligence. Materials & methods: Build machine learning and deep learning classification models and combine molecular docking and biological evaluation to screen ULK1 inhibitors from 13 million compounds. And molecular dynamics was used to explore the binding mechanism of active compounds. Results & conclusion: Possibly due to less available training data, machine learning models significantly outperform deep learning models. Among them, the Naive Bayes model has the best performance. Through virtual screening, we obtained three inhibitors with IC50 of μM level and they all bind well to ULK1. This study provides an efficient virtual screening model and three promising compounds for the study of ULK1 inhibitors.

Combined structure-based virtual screening and machine learning approach for the identification of potential dual inhibitors of ACC and DGAT2

Acetyl-coenzyme A carboxylase (ACC) and diacylglycerol acyltransferase 2 (DGAT2) are recognized as potential therapeutic targets for nonalcoholic fatty liver disease (NAFLD). Inhibitors targeting ACC and DGAT2 have exhibited the capacity to reduce hepatic fat in individuals afflicted with NAFLD. However, there are no reports of dual inhibitors targeting ACC and DGAT2 for the treatment of NAFLD. Here, we aimed to identify potential dual inhibitors of ACC and DGAT2 using an integrated in silico approach. Machine learning-based virtual screening of commercial molecule databases yielded 395,729 hits, which were subsequently subjected to molecular docking aimed at both the ACC and DGAT2 binding sites. Based on the docking scores, nine compounds exhibited robust interactions with critical residues of both ACC and DGAT2, displaying favorable drug-like features. Molecular dynamics simulations (MDs) unveiled the substantial impact of these compounds on the conformational dynamics of the proteins. Furthermore, binding free energy assessments highlighted the notable binding affinities of specific compounds (V003-8107, G340-0503, Y200-1700, E999-1199, V003-6429, V025-4981, V006-1474, V025-0499, and V021-8916) to ACC and DGAT2. The compounds proposed in this study, identified using a multifaceted computational strategy, warrant experimental validation as potential dual inhibitors of ACC and DGAT2, with implications for the future development of novel drugs targeting NAFLD.

Developing a Semi-Supervised Approach Using a PU-Learning-Based Data Augmentation Strategy for Multitarget Drug Discovery

Multifactorial diseases demand therapeutics that can modulate multiple targets for enhanced safety and efficacy, yet the clinical approval of multitarget drugs remains rare. The integration of machine learning (ML) and deep learning (DL) in drug discovery has revolutionized virtual screening. This study investigates the synergy between ML/DL methodologies, molecular representations, and data augmentation strategies. Notably, we found that SVM can match or even surpass the performance of state-of-the-art DL methods. However, conventional data augmentation often involves a trade-off between the true positive rate and false positive rate. To address this, we introduce Negative-Augmented PU-bagging (NAPU-bagging) SVM, a novel semi-supervised learning framework. By leveraging ensemble SVM classifiers trained on resampled bags containing positive, negative, and unlabeled data, our approach is capable of managing false positive rates while maintaining high recall rates. We applied this method to the identification of multitarget-directed ligands (MTDLs), where high recall rates are critical for compiling a list of interaction candidate compounds. Case studies demonstrate that NAPU-bagging SVM can identify structurally novel MTDL hits for ALK-EGFR with favorable docking scores and binding modes, as well as pan-agonists for dopamine receptors. The NAPU-bagging SVM methodology should serve as a promising avenue to virtual screening, especially for the discovery of MTDLs.

Machine learning-guided design of potent darunavir analogs targeting HIV-1 proteases: A computational approach for antiretroviral drug discovery

In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type-1 (HIV-1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one-dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild-type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy (ΔΔ G bind ) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model-guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.

Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists

Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

Epidermal growth factor receptor dual-target inhibitors as a novel therapy for cancer: A review

Overexpression of the epidermal growth factor receptor (EGFR) has been linked to several human cancers, including esophageal cancer, pancreatic cancer, anal cancer, breast cancer, and lung cancer, particularly non-small cell lung cancer (NSCLC). Therefore, EGFR has emerged as a critical target for treating solid tumors. Many 1st-, 2nd-, 3rd-, and 4th-generation EGFR single-target inhibitors with clinical efficacy have been designed and synthesized in recent years. Drug resistance caused by EGFR mutations has posed a significant challenge to the large-scale clinical application of EGFR single-target inhibitors and the discovery of novel EGFR inhibitors. Therapeutic methods for overcoming multipoint EGFR mutations are still needed in medicine. EGFR dual-target inhibitors are more promising than single-target inhibitors as they have a lower risk of drug resistance, higher efficacy, lower dosage, and fewer adverse events. EGFR dual-target inhibitors have been developed sequentially to date, providing new options for remission in patients with previously untreatable malignancies and laying the groundwork for a future generation of compounds. This paper introduces the EGFR family proteins and their synergistic effects with other anticancer targets, and provides a comprehensive review of the development of EGFR dual-target inhibitors in cancer, as well as the opportunities and challenges associated with those fields.

StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction

The B-rapidly accelerated fibrosarcoma (BRAF) is a proto-oncogene that plays a vital role in cell signaling and growth regulation. Identifying a potent BRAF inhibitor can enhance therapeutic success in high-stage cancers, particularly metastatic melanoma. In this study, we proposed a stacking ensemble learning framework for the accurate prediction of BRAF inhibitors. We obtained 3857 curated molecules with BRAF inhibitory activity expressed as a predicted half-maximal inhibitory concentration value (pIC₅₀) from the ChEMBL database. Twelve molecular fingerprints from PaDeL-Descriptor were calculated for model training. Three machine learning algorithms including extreme gradient boosting, support vector regression, and multilayer perceptron were utilized for constructing new predictive features (PFs). The meta-ensemble random forest regression, called StackBRAF, was created based on the 36 PFs. The StackBRAF model achieves lower mean absolute error (MAE) and higher coefficient of determination (R² and Q²) than the individual baseline models. The stacking ensemble learning model provides good y-randomization results, indicating a strong correlation between molecular features and pIC₅₀. An applicability domain of the model with an acceptable Tanimoto similarity score was also defined. Moreover, a large-scale high-throughput screening of 2123 FDA-approved drugs against the BRAF protein was successfully demonstrated using the StackBRAF algorithm. Thus, the StackBRAF model proved beneficial as a drug design algorithm for BRAF inhibitor drug discovery and drug development.

An Interpretable Multitask Framework BiLAT Enables Accurate Prediction of Cyclin-Dependent Protein Kinase Inhibitors

The cyclin-dependent protein kinases (CDKs) are protein-serine/threonine kinases with crucial effects on the regulation of cell cycle and transcription. CDKs can be a hallmark of cancer since their excessive expression could lead to impaired cell proliferation. However, the selectivity profile of most developed CDK inhibitors is not enough, which have hindered the therapeutic use of CDK inhibitors. In this study, we propose a multitask deep learning framework called BiLAT based on SMILES representation for the prediction of the inhibitory activity of molecules on eight CDK subtypes (CDK1, 2, 4-9). The framework is mainly composed of an improved bidirectional long short-term memory module BiLSTM and the encode layer of the Transformer framework. Additionally, the data enhancement method of SMILES enumeration is applied to improve the performance of the model. Compared with baseline predictive models based on three conventional machine learning methods and two multitask deep learning algorithms, BiLAT achieves the best performance with the highest average AUC, ACC, F1-score, and MCC values of 0.938, 0.894, 0.911, and 0.715 for the test set. Moreover, we constructed a targeted external data set CDK-Dec for the CDK family, which mainly contains bait values screened by 3D similarity with active compounds. This dataset was utilized in the subsequent evaluation of our model. It is worth mentioning that the BiLAT model is interpretable and can be used by chemists to design and synthesize compounds with improved activity. To further verify the generalization ability of the multitask BiLAT model, we also conducted another evaluation on three public datasets (Tox21, ClinTox, and SIDER). Compared with several currently popular models, BiLAT shows the best performance on two datasets. These results indicate that BiLAT is an effective tool for accelerating drug discovery.

DeepCancerMap: A versatile deep learning platform for target- and cell-based anticancer drug discovery

Discovering new anticancer drugs has been widely concerned and remains an open challenge. Target- and phenotypic-based experimental screening represent two mainstream anticancer drug discovery methods, which suffer from time-consuming, labor-intensive, and high experimental costs. In this study, we collected 485,900 compounds involving in 3,919,974 bioactivity records against 426 anticancer targets and 346 cancer cell lines from academic literature, as well as 60 tumor cell lines from NCI-60 panel. A total of 832 classification models (426 target- and 406 cell-based predictive models) were then constructed to predict the inhibitory activity of compounds against targets and tumor cell lines using FP-GNN deep learning method. Compared to the classical machine learning and deep learning methods, the FP-GNN models achieve considerable overall predictive performance, with the highest AUC values of 0.91, 0.88, 0.91 for the test sets of targets, academia-sourced and NCI-60 cancer cell lines, respectively. A user-friendly webserver called DeepCancerMap and its local version were developed based on these high-quality models, enabling users to perform anticancer drug discovery-related tasks including large-scale virtual screening, profiling prediction of anticancer agents, target fishing, and drug repositioning. We anticipate this platform to accelerate the discovery of anticancer drugs in the field. DeepCancerMap is freely available at https://deepcancermap.idruglab.cn.

Machine learning facilitated structural activity relationship approach for the discovery of novel inhibitors targeting EGFR

This research manuscript aims to find the most effective epidermal growth factor receptor (EGFR) inhibitors from millions of in house compounds through Machine Learning (ML) techniques. ML-based structure activity relationship (SAR) models were validated to predict biological activity of untested novel molecules. Six ML algorithms, including k nearest neighbour (KNN), decision tree (DT), Logistic Regression, support vector machine (SVM), multilinear regression (MLR), and random forest (RF), were used to build for activity prediction. Among these, RF classifier (accuracy for train and test set is 90% and 81%) and RF regressor (R2 and MSE for trainset is 0.83 and 0.29 and for test set, 0.69 and 0.46) showed good predictive performance. Also, the six most essential features that affect the biological activity parameter and highly contribute to model development were successfully selected by the variable importance technique. RF regression model was used to predict the biological activity expressed as pIC50 of nearly ten million molecules while RF classification model classifies those molecules into active, moderately active, and least active according to their predicted pIC50. Based on two models, thousand molecules from million molecules with higher predicted pIC50 values and classified as active were selected for molecular docking. Based on the docking scores, predicted pIC50, and binding interactions with MET769 residue, compounds, i.e., Zinc257233137, Zinc257232249, and Zinc101379788, were identified as potential EGFR inhibitors with predicted pIC50 7.72, 7.85, and 7.70. Dynamics studies were also performed on Zinc257233137 to illustrate that it has good binding free energy and stable hydrogen bonding interactions with EGFR. These molecules can be used for further research and proved to be the novel drugs for EGFR in cancer treatment.Communicated by Ramaswamy H. Sarma.

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

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 IC₅₀ 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.

Alpinumisoflavone against cancer pro-angiogenic targets: In silico, In vitro, and In ovo evaluation

BACKGROUND

Breast cancer is currently the world's most predominant malignancy. In cancer progression, angiogenesis is a requirement for tumor growth and metastasis.Alpinumisoflavone (AIF), a bioactive isoflavonoid, exhibited good binding affinity with the angiogenesis pathway's druggable target through molecular docking.

OBJECTIVES

To confirm AIF's angiogenesis inhibitory activity, cytotoxic potential toward breast cancer cells, and druggability.

METHODS

Antiangiogenic activity was evaluated in six pro-angiogenic proteins in vitro, duck chorioallantoic membrane (CAM) in ovo, molecular docking and druggability in silico.

RESULTS

Findings showed that AIF significantly inhibited (p =  < 0.001) the HER2(IC50 = 2.96 µM), VEGFR-2(IC50 = 4.80 µM), MMP-9(IC50 = 23.00 µM), FGFR4(IC50 = 57.65 µM), EGFR(IC50 = 92.06 µM) and RET(IC50 =  > 200 µM) activity in vitro.AIF at 25 µM-200 µM significantly inhibited (p =  < 0.001) the total number of branch points (IC50 = 14.25 μM) and mean length of tubule complexes (IC50 = 3.52 μM) of duck CAM comparable (p =  > 0.001) with the positive control 200 µM celecoxib on both parameters.AIF inhibited the growth of the estrogen-receptor-positive (ER +) human breast cancer cells (MCF-7) by 44.92 ± 1.79% at 100 µM while presenting less toxicity to human dermal fibroblast neonatal (HDFn) normal cells.The positive control 100 µM doxorubicin showed 86.66 ± 0.93% and 92.97 ± 1.27% inhibition with MCF-7 (IC50 = 3.62 μM) and HDFn, (IC50 = 27.16 μM) respectively.In docking, AIF has the greatest in silico binding affinity on HER2 (-10.9 kcal/mol) among the key angiogenic molecules tested. In silico rat oral LD50 calculation indicates that AIF is moderate to slightly toxic at 146.4 mg/kg with 1.1 g/kg and 20.1 mg/kg upper and lower 95% confidence limits. Lastly, it sufficiently complies with Lipinski's, Veber's, Egan's, Ghose's, and Muegge's Rule, supporting its oral drug-like property.

CONCLUSION

This study revealed that AIF possesses characteristics of a phytoestrogen compound with significant binding affinity, inhibitory activity against pro-angiogenic proteins, and cytotoxic potential against ER + breast cancer cells.The acceptable and considerable safety and drug-likeness profiles of AIF are worthy of further confirmation in vivo and advanced pre-clinical studies so that AIF can be elevated as a promising molecule for breast cancer therapy.

In-silico discovery of inhibitors against human papillomavirus E1 protein

High-risk (HR) Human papillomavirus (e.g. HPV16 and HPV18) causes approximately two-thirds of all cervical cancers in women. Although the first and second-generation vaccines confer some protection against individuals, there are no approved drugs to treat HR-HPV infections to-date. The HPV E1 protein is an attractive drug target because the protein is highly conserved across all HPV types and is crucial for the regulation of viral DNA replication. Hence, we used the Random Forest algorithm to construct a Quantitative-Structure Activity Relationship (QSAR) model to predict the potential inhibitors against the HPV E1 protein. Our QSAR classification model achieved an accuracy of 87.5%, area under the receiver operating characteristic curve of 1.00, and F-measure of 0.87 when evaluated using an external test set. We conducted a drug repurposing campaign by deploying the model to screen the Drugbank database. The top three compounds, namely Cinalukast, Lobeglitazone, and Efatutazone were analyzed for their cell membrane permeability, toxicity, and carcinogenicity. Finally, these three compounds were subjected to molecular docking and 200 ns-long Molecular Dynamics (MD) simulations. The predicted binding free energies for the candidates were calculated using the MM-GBSA method. The binding free energies for Cinalukast, Lobeglitazone, and Efatutazone were -37.84 kcal/mol, -25.30 kcal/mol, and -29.89 kcal/mol respectively. Therefore, we propose their chemical scaffolds for future rational design of E1 inhibitors.Communicated by Ramaswamy H. Sarma.

Artificial intelligence in virtual screening: models versus experiments

A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.

Signaling Pathway and Small-Molecule Drug Discovery of FGFR: A Comprehensive Review

Targeted therapy is a groundbreaking innovation for cancer treatment. Among the receptor tyrosine kinases, the fibroblast growth factor receptors (FGFRs) garnered substantial attention as promising therapeutic targets due to their fundamental biological functions and frequently observed abnormality in tumors. In the past 2 decades, several generations of FGFR kinase inhibitors have been developed. This review starts by introducing the biological basis of FGF/FGFR signaling. It then gives a detailed description of different types of small-molecule FGFR inhibitors according to modes of action, followed by a systematic overview of small-molecule-based therapies of different modalities. It ends with our perspectives for the development of novel FGFR inhibitors.

Development of Dual Inhibitors Targeting Epidermal Growth Factor Receptor in Cancer Therapy

Epidermal growth factor receptor (EGFR) is of great significance in mediating cell signaling transduction and tumor behaviors. Currently, third-generation inhibitors of EGFR, especially osimertinib, are at the clinical frontier for the treatment of EGFR-mutant non-small-cell lung cancer (NSCLC). Regrettably, the rapidly developing drug resistance caused by EGFR mutations and the compensatory mechanism have largely limited their clinical efficacy. Given the synergistic effect between EGFR and other compensatory targets during tumorigenesis and tumor development, EGFR dual-target inhibitors are promising for their reduced risk of drug resistance, higher efficacy, lower dosage, and fewer adverse events than those of single-target inhibitors. Hence, we present the synergistic mechanism underlying the role of EGFR dual-target inhibitors against drug resistance, their structure-activity relationships, and their therapeutic potential. Most importantly, we emphasize the optimal target combinations and design strategies for EGFR dual-target inhibitors and provide some perspectives on new challenges and future directions in this field.

Discovery of Kinase and Carbonic Anhydrase Dual Inhibitors by Machine Learning Classification and Experiments

A multi-target small molecule modulator is advantageous for treating complicated diseases such as cancers. However, the strategy and application for discovering a multi-target modulator have been less reported. This study presents the dual inhibitors for kinase and carbonic anhydrase (CA) predicted by machine learning (ML) classifiers, and validated by biochemical and biophysical experiments. ML trained by CA I and CA II inhibitor molecular fingerprints predicted candidates from the protein-specific bioactive molecules approved or under clinical trials. For experimental tests, three sulfonamide-containing kinase inhibitors, 5932, 5946, and 6046, were chosen. The enzyme assays with CA I, CA II, CA IX, and CA XII have allowed the quantitative comparison in the molecules’ inhibitory activities. While 6046 inhibited weakly, 5932 and 5946 exhibited potent inhibitions with 100 nM to 1 μM inhibitory constants. The ML screening was extended for finding CAs inhibitors of all known kinase inhibitors. It found XMU-MP-1 as another potent CA inhibitor with an approximate 30 nM inhibitory constant for CA I, CA II, and CA IX. Differential scanning fluorimetry confirmed the direct interaction between CAs and small molecules. Cheminformatics studies, including docking simulation, suggest that each molecule possesses two separate functional moieties: one for interaction with kinases and the other with CAs.

Assisting Multitargeted Ligand Affinity Prediction of Receptor Tyrosine Kinases Associated Nonsmall Cell Lung Cancer Treatment with Multitasking Principal Neighborhood Aggregation

A multitargeted therapeutic approach with hybrid drugs is a promising strategy to enhance anticancer efficiency and overcome drug resistance in nonsmall cell lung cancer (NSCLC) treatment. Estimating affinities of small molecules against targets of interest typically proceeds as a preliminary action for recent drug discovery in the pharmaceutical industry. In this investigation, we employed machine learning models to provide a computationally affordable means for computer-aided screening to accelerate the discovery of potential drug compounds. In particular, we introduced a quantitative structure–activity-relationship (QSAR)-based multitask learning model to facilitate an in silico screening system of multitargeted drug development. Our method combines a recently developed graph-based neural network architecture, principal neighborhood aggregation (PNA), with a descriptor-based deep neural network supporting synergistic utilization of molecular graph and fingerprint features. The model was generated by more than ten-thousands affinity-reported ligands of seven crucial receptor tyrosine kinases in NSCLC from two public data sources. As a result, our multitask model demonstrated better performance than all other benchmark models, as well as achieving satisfying predictive ability regarding applicable QSAR criteria for most tasks within the model’s applicability. Since our model could potentially be a screening tool for practical use, we have provided a model implementation platform with a tutorial that is freely accessible hence, advising the first move in a long journey of cancer drug development.

Enhancing preclinical drug discovery with artificial intelligence

Artificial intelligence (AI) is becoming an integral part of drug discovery. It has the potential to deliver across the drug discovery and development value chain, starting from target identification and reaching through clinical development. In this review, we provide an overview of current AI technologies and a glimpse of how AI is reimagining preclinical drug discovery by highlighting examples where AI has made a real impact. Considering the excitement and hyperbole surrounding AI in drug discovery, we aim to present a realistic view by discussing both opportunities and challenges in adopting AI in drug discovery.

Gaultheria leucocarpa var. yunnanensis for Treating Rheumatoid Arthritis-An Assessment Combining Machine Learning-Guided ADME Properties Prediction, Network Pharmacology, and Pharmacological Assessment

Background: Dianbaizhu (Gaultheria leucocarpa var. yunnanensis), a traditional Chinese/ethnic medicine (TC/EM), has been used to treat rheumatoid arthritis (RA) for a long time. The anti-rheumatic arthritis fraction (ARF) of G. yunnanensis has significant anti-inflammatory and analgesic activities and is mainly composed of methyl salicylate glycosides, flavonoids, organic acids, and others. The effective ingredients and rudimentary mechanism of ARF remedying RA have not been elucidated to date. Purpose: The aim of the present study is to give an insight into the effective components and mechanisms of Dianbaizhu in ameliorating RA, based on the estimation of the absorption, distribution, metabolism, and excretion (ADME) properties, analysis of network pharmacology, and in vivo and in vitro validations. Study design and methods: The IL-1β-induced human fibroblast-like synoviocytes of RA (HFLS-RA) model and adjuvant-induced arthritis in the rat model were adopted to assess the anti-RA effect of ARF. The components in ARF were identified by using UHPLC-LTQ-Orbitrap-MSⁿ. The quantitative structure-activity relationship (QSAR) models were developed by using five machine learning algorithms, alone or in combination with genetic algorithms for predicting the ADME properties of ARF. The molecular networks and pathways presumably referring to the therapy of ARF on RA were yielded by using common databases and visible software, and the experimental validations of the key targets conducted in vitro. Results: ARF effectively relieved RA in vivo and in vitro. The five optimized QSAR models that were developed showed robustness and predictive ability. The characterized 48 components in ARF had good biological potency. Four key signaling pathways were obtained, which were related to both cytokine signaling and cell immune response. ARF suppressed IL-1β-induced expression of EGFR, MMP 9, IL2, MAPK14, and KDR in the HFLS-RA . Conclusions: ARF has good druggability and high exploitation potential. Methyl salicylate glycosides and flavonoids play essential roles in attuning RA. ARF may partially attenuate RA by regulating the expression of multi-targets in the inflammation-immune system. These provide valuable information to rationalize ARF and other TC/EMs in the treatment of RA.

Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations

Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter-property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.

Recent advances of dual FGFR inhibitors as a novel therapy for cancer

Fibroblast growth factor receptor (FGFR) includes four highly conserved transmembrane receptor tyrosine kinases (FGFR1-4). FGF and FGFR regulate many biological processes, such as angiogenesis, wound healing and tissue regeneration. The abnormal expression of FGFR is related to the tumorigenesis, tumor progression and drug resistance of anti-tumor treatments in many types of tumors. Nowadays there are many anti-cancer drugs targeting FGFR. However, traditional single-target anti-tumor drugs are easy to acquire drug resistance. The therapeutic effect can be enhanced by simultaneously inhibiting FGFR and another target (such as VEGFR, EGFR, PI3K, CSF-1R, etc.). We know drug combination can bring problems such as drug interactions. Therefore, the development of FGFR dual target inhibitors is an important direction. In this paper, we reviewed the research on dual FGFR inhibitors in recent years and made brief comments on them.