Discovery of indoleamine 2,3-dioxygenase inhibitors using machine learning based virtual screening

Discovery of novel SOS1 inhibitors using machine learning

Overactivation of the rat sarcoma virus (RAS) signaling is responsible for 30% of all human malignancies. Son of sevenless 1 (SOS1), a crucial node in the RAS signaling pathway, could modulate RAS activation, offering a promising therapeutic strategy for RAS-driven cancers. Applying machine learning (ML)-based virtual screening (VS) on small-molecule databases, we selected a random forest (RF) regressor for its robustness and performance. Screening was performed with the L-series and EGFR-related datasets, and was extended to the Chinese National Compound Library (CNCL) with more than 1.4 million compounds. In addition to a series of documented SOS1-related molecules, we uncovered nine compounds that have an unexplored chemical framework and displayed inhibitory activity, with the most potent achieving more than 50% inhibition rate in the KRAS G12C/SOS1 PPI assay and an IC50 value in the proximity of 20 μg mL-1. Compared with the manner that known inhibitory agents bind to the target, hit compounds represented by CL01545365 occupy a unique pocket in molecular docking. An in silico drug-likeness assessment suggested that the compound has moderately favorable drug-like properties and pharmacokinetic characteristics. Altogether, our findings strongly support that, characterized by the distinctive binding modes, the recognition of novel skeletons from the carboxylic acid series could be candidates for developing promising SOS1 inhibitors.

Prediction of IDO1 Inhibitors by a Fingerprint-Based Stacking Ensemble Model Named IDO1Stack

Indoleamine 2,3-dioxygenase 1 (IDO1) is viewed as an extremely promising target for cancer immunotherapy. Here, we proposed a two-layer stacking ensemble model, IDO1Stack, that can efficiently predict IDO1 inhibitors. First, we constructed a series of classification models based on five machine learning algorithms and eight molecular characterization methods. Then, a stacking ensemble model was built using the top five models as the base classifier and logistic regression as the meta-classifier. The areas under the receiver operating characteristic curve (AUC) of IDO1Stack on the test set and external validation set were 0.952 and 0.918, respectively. Furthermore, we computed the applicability domain and privileged substructures of the model and interpreted the model using SHapley Additive exPlanations (SHAP). It is expected that IDO1Stack can well study the interaction between target and ligand, providing practitioners with a reliable tool for rapid screening and discovery of IDO1 inhibitors.

Drug Repurposing to Circumvent Immune Checkpoint Inhibitor Resistance in Cancer Immunotherapy

Immune checkpoint inhibitors (ICI) have achieved unprecedented clinical success in cancer treatment. However, drug resistance to ICI therapy is a major hurdle that prevents cancer patients from responding to the treatment or having durable disease control. Drug repurposing refers to the application of clinically approved drugs, with characterized pharmacological properties and known adverse effect profiles, to new indications. It has also emerged as a promising strategy to overcome drug resistance. In this review, we summarized the latest research about drug repurposing to overcome ICI resistance. Repurposed drugs work by either exerting immunostimulatory activities or abolishing the immunosuppressive tumor microenvironment (TME). Compared to the de novo drug design strategy, they provide novel and affordable treatment options to enhance cancer immunotherapy that can be readily evaluated in the clinic. Biomarkers are exploited to identify the right patient population to benefit from the repurposed drugs and drug combinations. Phenotypic screening of chemical libraries has been conducted to search for T-cell-modifying drugs. Genomics and integrated bioinformatics analysis, artificial intelligence, machine and deep learning approaches are employed to identify novel modulators of the immunosuppressive TME.

Potential Inhibitors of Monkeypox Virus Revealed by Molecular Modeling Approach to Viral DNA Topoisomerase I

The monkeypox outbreak has become a global public health emergency. The lack of valid and safe medicine is a crucial obstacle hindering the extermination of orthopoxvirus infections. The identification of potential inhibitors from natural products, including Traditional Chinese Medicine (TCM), by molecular modeling could expand the arsenal of antiviral chemotherapeutic agents. Monkeypox DNA topoisomerase I (TOP1) is a highly conserved viral DNA repair enzyme with a small size and low homology to human proteins. The protein model of viral DNA TOP1 was obtained by homology modeling. The reliability of the TOP1 model was validated by analyzing its Ramachandran plot and by determining the compatibility of the 3D model with its sequence using the Verify 3D and PROCHECK services. In order to identify potential inhibitors of TOP1, an integrated library of 4103 natural products was screened via Glide docking. Surface Plasmon Resonance (SPR) was further implemented to assay the complex binding affinity. Molecular dynamics simulations (100 ns) were combined with molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations to reveal the binding mechanisms of the complex. As a result, three natural compounds were highlighted as potential inhibitors via docking-based virtual screening. Rosmarinic acid, myricitrin, quercitrin, and ofloxacin can bind TOP1 with KD values of 2.16 μM, 3.54 μM, 4.77 μM, and 5.46 μM, respectively, indicating a good inhibitory effect against MPXV. The MM/PBSA calculations revealed that rosmarinic acid had the lowest binding free energy at -16.18 kcal/mol. Myricitrin had a binding free energy of -13.87 kcal/mol, quercitrin had a binding free energy of -9.40 kcal/mol, and ofloxacin had a binding free energy of -9.64 kcal/mol. The outputs (RMSD/RMSF/Rg/SASA) also indicated that the systems were well-behaved towards the complex. The selected compounds formed several key hydrogen bonds with TOP1 residues (TYR274, LYS167, GLY132, LYS133, etc.) via the binding mode analysis. TYR274 was predicted to be a pivotal residue for compound interactions in the binding pocket of TOP1. The results of the enrichment analyses illustrated the potential pharmacological networks of rosmarinic acid. The molecular modeling approach may be acceptable for the identification and design of novel poxvirus inhibitors; however, further studies are warranted to evaluate their therapeutic potential.

CADD, AI and ML in drug discovery: A comprehensive review

Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.

Assessment of the Generalization Abilities of Machine-Learning Scoring Functions for Structure-Based Virtual Screening

In structure-based virtual screening (SBVS), it is critical that scoring functions capture protein-ligand atomic interactions. By focusing on the local domains of ligand binding pockets, a standardized pocket Pfam-based clustering (Pfam-cluster) approach was developed to assess the cross-target generalization ability of machine-learning scoring functions (MLSFs). Subsequently, 12 typical MLSFs were evaluated using random cross-validation (Random-CV), protein sequence similarity-based cross-validation (Seq-CV), and pocket Pfam-based cross-validation (Pfam-CV) methods. Surprisingly, all of the tested models showed decreased performances from Random-CV to Seq-CV to Pfam-CV experiments, not showing satisfactory generalization capacity. Our interpretable analysis suggested that the predictions on novel targets by MLSFs were dependent on buried solvent-accessible surface area (SASA)-related features of complex structures, with greater predicted binding affinities on complexes owning larger protein-ligand interfaces. By combining buried SASA-related features with target-specific patterns that were only shared among structurally similar compounds in the same cluster, the random forest (RF)-Score attained a good performance in the Random-CV test. Based on these findings, we strongly advise assessing the generalization ability of MLSFs with the Pfam-cluster approach and being cautious with the features learned by MLSFs.

Indoleamine 2, 3-dioxygenase 1 inhibitory compounds from natural sources

L-tryptophan metabolism is involved in the regulation of many important physiological processes, such as, immune response, inflammation, and neuronal function. Indoleamine 2, 3-dioxygenase 1 (IDO1) is a key enzyme that catalyzes the first rate-limiting step of tryptophan conversion to kynurenine. Thus, inhibiting IDO1 may have therapeutic benefits for various diseases, such as, cancer, autoimmune disease, and depression. In the search for potent IDO1 inhibitors, natural quinones were the first reported IDO1 inhibitors with potent inhibitory activity. Subsequently, natural compounds with diverse structures have been found to have anti-IDO1 inhibitory activity. In this review, we provide a summary of these natural IDO1 inhibitors, which are classified as quinones, polyphenols, alkaloids and others. The overview of in vitro IDO1 inhibitory activity of natural compounds will help medicinal chemists to understand the mode of action and medical benefits of them. The scaffolds of these natural compounds can also be used for further optimization of potent IDO1 inhibitors.

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.

A review on machine learning approaches and trends in drug discovery

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Tanshinones: An Update in the Medicinal Chemistry in Recent 5 Years

Tanshinones are an important type of natural products isolated from Salvia miltiorrhiza Bunge with various bioactivities. Tanshinone IIa, cryptotanshinone and tanshinone I are three kinds of tanshinones which have been widely investigated. Particularly, sodium tanshinone IIa sulfonate is a water-soluble derivative of tanshinone IIa and it is used in clinical in China for treating cardiovascular diseases. In recent years, there are increasing interests in the investigation of tanshinones derivatives in various diseases. This article presents a review of the anti-atherosclerotic effects, cardioprotective effects, anticancer activities, antibacterial activities and antiviral activities of tanshinones and structural modification work in recent years.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Machine and deep learning approaches for cancer drug repurposing

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

Development of Indoleamine 2,3-Dioxygenase 1 Inhibitors for Cancer Therapy and Beyond: A Recent Perspective

Indoleamine 2,3-dioxygenase 1 (IDO1) has received increasing attention due to its immunosuppressive function in connection with various diseases, including cancer. A recent increase in the understanding of IDO1 has significantly contributed to the discovery of numerous novel inhibitors, but the latest clinical outcomes raised questions and have indicated a future direction of IDO1 inhibition for therapeutic approaches. Herein, we present a comprehensive review of IDO1, discussing the latest advances in understanding the IDO1 structure and mechanism, an overview of recent IDO1 inhibitor discoveries and potential therapeutic applications to provide helpful information for medicinal chemists investigating IDO1 inhibitors.

New Insights from Crystallographic Data: Diversity of Structural Motifs and Molecular Recognition Properties between Groups of IDO1 Structures

A large number of crystallographic structures of IDO1 in different ligand-bound and -unbound states have been disclosed over the last decade. Yet, only a few of them have been exploited for structure-based drug design (SBDD) campaigns. In this study, we analyzed the structural motifs and molecular-recognition properties of three groups of IDO1 structures: 1) structures containing the heme group and inhibitors in the catalytic site; 2) heme-free structures of IDO1; 3) substrate-bound structures of IDO1. The results suggest that unrelated conformations of the enzyme have been solved with different ligand-induced changes of secondary motifs that localize even in regions remote from the catalytic site. Moreover, the study identified an uncharted region of molecular-recognition space covered by IDO1 binding sites that could guide the selection of diverse structures for additional SBDD studies aimed at the identification of novel lead compounds with differentiated chemical scaffolds.

Discovery and characterization of natural products as novel indoleamine 2,3-dioxygenase 1 inhibitors through high-throughput screening

Indoleamine 2,3-dioxygenase 1 (IDO1) is emerging as a promising therapeutic target for the treatment of malignant tumors characterized by dysregulated tryptophan metabolism. However, the antitumor efficacy of existing small-molecule IDO1 inhibitors is still unsatisfactory, and the underlying mechanism remains largely undefined. To identify novel IDO1 inhibitors, an in-house natural product library of 2000 natural products was screened for inhibitory activity against recombinant human IDO1. High-throughput fluorescence-based screening identified 79 compounds with inhibitory activity > 30% at 20 μM. Nine natural products were further confirmed to inhibit IDO1 activity by > 30% using Ehrlich’s reagent reaction. Compounds 2, 7, and 8 were demonstrated to inhibit IDO1 activity in a cellular context. Compounds 2 and 7 were more potent against IDO1 than TDO2 in the enzymatic assay. The kinetic studies showed that compound 2 exhibited noncompetitive inhibition, whereas compounds 7 and 8 were graphically well matched with uncompetitive inhibition. Compounds 7 and 8 were found to bind to the ferric-IDO1 enzyme. Docking stimulations showed that the naphthalene ring of compound 8 formed “T-shaped” π–π interactions with Phe-163 and that the 6-methyl-naphthalene group formed additional hydrophobic interactions with IDO1. Compound 8 was identified as a derivative of tanshinone, and preliminary SAR analysis indicated that tanshinone derivatives may be promising hits for the development of IDO1 inhibitors. This study provides new clues for the discovery of IDO1/TDO2 inhibitors with novel scaffolds.

Inflammation in cancer and depression: a starring role for the kynurenine pathway

Depression is a common comorbidity in cancer cases, but this is not only due to the emotional distress of having a life-threatening disease. A common biological mechanism, involving a dysregulated immune system, seems to underpin this comorbidity. In particular, the activation of the kynurenine pathway of tryptophan degradation due to inflammation may play a key role in the development and persistence of both diseases. As a consequence, targeting enzymes involved in this pathway offers a unique opportunity to develop new strategies to treat cancer and depression at once. In this work, we provide a systematic review of the evidence up to date on the kynurenine pathway role in linking depression and cancer and on clinical implications of this evidence. In particular, complications due to chemotherapy are discussed, as well as the potential antidepressant efficacy of novel immunotherapies for cancer.

Discovery of Indoleamine 2,3-Dioxygenase 1 (IDO-1) Inhibitors Based on Ortho-Naphthaquinone-Containing Natural Product

There is great interest in developing small molecules agents capable of reversing tumor immune escape to restore the body’s immune system. As an immunosuppressive enzyme, indoleamine 2,3-dioxygenase 1 (IDO-1) is considered a promising target for oncology immunotherapy. Currently, none of IDO-1 inhibitors have been launched for clinical practice yet. Thus, the discovery of new IDO-1 inhibitors is still in great demand. Herein, a series of diverse ortho-naphthaquinone containing natural product derivatives were synthesized as novel IDO-1 inhibitors. Among them, 1-ene-3-ketone-17-hydroxyl derivative 12 exhibited significantly improved enzymatic and cellular inhibitory activity against IDO-1 when compared to initial lead compounds. Besides, the molecular docking study disclosed that the two most potent compounds 11 and 12 have more interactions within the binding pocket of IDO-1 via hydrogen-bonding, which may account for their higher IDO-1 inhibitory activity.