QSAR modeling: where have you been? Where are you going to?

A brief guide to machine learning for antibiotic discovery

Rising antibiotic resistance and an alarmingly lean antibiotic pipeline require the adoption of novel approaches to rapidly discover new structural and functional classes of antibiotics. Excitingly, algorithmic approaches to antibiotic discovery are sufficiently advanced to meaningfully influence the antibiotic discovery process. Indeed, once trained on high-quality datasets, contemporary machine-learning and deep-learning models can be used to perform predictions for new antibiotics across vast chemical spaces, orders of magnitude more rapidly than compounds can be screened in the laboratory. This increases the probability of discovering new antibiotics with desirable properties. In this short review, we briefly describe the utility of contemporary machine-learning and deep-learning approaches to guide the discovery of new small-molecule antibiotics and unidentified natural products. We then propose a call to action for more open sharing of high-quality screening datasets to accelerate the rate at which forthcoming antibiotic-prediction models can be trained. Together, we aim to introduce antibiotic discoverers to a sample of recent applications of contemporary algorithmic methods to facilitate the wider adoption of these powerful computational approaches.

Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation

Recently, graph neural networks (GNNs) have revolutionized the field of chemical property prediction and achieved state-of-the-art results on benchmark data sets. Compared with the traditional descriptor- and fingerprint-based QSAR models, GNNs can learn task related representations, which completely gets rid of the rules defined by experts. However, due to the lack of useful prior knowledge, the prediction performance and interpretability of the GNNs may be affected. In this study, we introduced a new GNN model called RG-MPNN for chemical property prediction that integrated pharmacophore information hierarchically into message-passing neural network (MPNN) architecture, specifically, in the way of pharmacophore-based reduced-graph (RG) pooling. RG-MPNN absorbed not only the information of atoms and bonds from the atom-level message-passing phase, but also the information of pharmacophores from the RG-level message-passing phase. Our experimental results on eleven benchmark and ten kinase data sets showed that our model consistently matched or outperformed other existing GNN models. Furthermore, we demonstrated that applying pharmacophore-based RG pooling to MPNN architecture can generally help GNN models improve the predictive power. The cluster analysis of RG-MPNN representations and the importance analysis of pharmacophore nodes will help chemists gain insights for hit discovery and lead optimization.

QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads

ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm−multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69−0.87, F = 403.46−292.11, etc., internal validation parameters; Q2LOO = 0.69−0.86, Q2LMO = 0.69−0.86, CCCcv = 0.82−0.93, etc., or external validation parameters Q2F1 = 0.64−0.82, Q2F2 = 0.63−0.82, Q2F3 = 0.65−0.81, R2ext = 0.65−0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.

Development of Novel Therapeutics for Schizophrenia Treatment Based on a Selective Positive Allosteric Modulation of α1-Containing GABAARs-In Silico Approach

For the development of atypical antipsychotics, the selective positive allosteric modulation of the ionotropic GABAA receptor (GABAAR) has emerged as a promising approach. In the presented research, two unrelated methods were used for the development of QSAR models for selective positive allosteric modulation of 1-containing GABAARs with derivatives of imidazo [1,2-a]-pyridine. The development of conformation-independent QSAR models, based on descriptors derived from local molecular graph invariants and SMILES notation, was achieved with the Monte Carlo optimization method. From the vast pool of 0D, 1D, and 2D molecule descriptors, the GA-MLR method developed additional QSAR models. Various statistical methods were utilised for the determination of the developed models' robustness, predictability, and overall quality, and according to the obtained results, all QSAR models are considered good. The molecular fragments that have a positive or negative impact on the studied activity were obtained from the studied molecules' SMILES notations, and according to the obtained results, nine novel compounds were designed. The binding affinities to GABAAR of designed compounds were assessed with the application of molecular docking studies and the obtained results showed a high correlation with results obtained from QSAR modeling. To assess all designed molecules' "drug-likeness", their physicochemical descriptors were computed and utilised for the prediction of medicinal chemistry friendliness, pharmacokinetic properties, ADME parameters, and druglike nature.

QSAR Evaluations to Unravel the Structural Features in Lysine-Specific Histone Demethylase 1A Inhibitors for Novel Anticancer Lead Development Supported by Molecular Docking, MD Simulation and MMGBSA

Using 84 structurally diverse and experimentally validated LSD1/KDM1A inhibitors, quantitative structure–activity relationship (QSAR) models were built by OECD requirements. In the QSAR analysis, certainly significant and understated pharmacophoric features were identified as critical for LSD1 inhibition, such as a ring Carbon atom with exactly six bonds from a Nitrogen atom, partial charges of lipophilic atoms within eight bonds from a ring Sulphur atom, a non-ring Oxygen atom exactly nine bonds from the amide Nitrogen, etc. The genetic algorithm–multi-linear regression (GA-MLR) and double cross-validation criteria were used to create robust QSAR models with high predictability. In this study, two QSAR models were developed, with fitting parameters like R² = 0.83–0.81, F = 61.22–67.96, internal validation parameters such as Q²_LOO = 0.79–0.77, Q²_LMO = 0.78–0.76, CCC_cv = 0.89–0.88, and external validation parameters such as, R2ext = 0.82 and CCCex = 0.90. In terms of mechanistic interpretation and statistical analysis, both QSAR models are well-balanced. Furthermore, utilizing the pharmacophoric features revealed by QSAR modelling, molecular docking experiments corroborated with the most active compound’s binding to the LSD1 receptor. The docking results are then refined using Molecular dynamic simulation and MMGBSA analysis. As a consequence, the findings of the study can be used to produce LSD1/KDM1A inhibitors as anticancer leads.

Conformational Effects on the Passive Membrane Permeability of Synthetic Macrocycles

Macrocyclic compounds (MCs) can have complex conformational properties that affect pharmacologically important behaviors such as membrane permeability. We measured the passive permeability of 3600 diverse nonpeptidic MCs and used machine learning to analyze the results. Incorporating selected properties based on the three-dimensional (3D) conformation gave models that predicted permeability with Q² = 0.81. A biased spatial distribution of polar versus nonpolar regions was particularly important for good permeability, consistent with a mechanism in which the initial insertion of nonpolar portions of a MC helps facilitate the subsequent membrane entry of more polar parts. We also examined effects on permeability of 800 substructural elements by comparing matched molecular pairs. Some substitutions were invariably beneficial or invariably deleterious to permeability, while the influence of others was highly contextual. Overall, the work provides insights into how the permeability of MCs is influenced by their 3D conformational properties and suggests design hypotheses for achieving macrocycles with high membrane permeability.

Escaping from the Cutoff Paradox by Accumulating Long-Chain Alcohols in the Cell Membrane

The mechanism for the cutoff, an activity cliff at which long-chain alcohols lose their biological effects, has not been elucidated. Highly hydrophobic oleyl alcohol (C_18:1) exists as a mixture of monomers and aggregated droplets in water. C_18:1 did not inhibit the yeast growth but inhibited the growth of the slime mold without a cell wall. C_18:1 exhibited toxicity to the yeast protoplast, which was enhanced by polyethylene glycol, a fusogen. Therefore, direct interactions of C_18:1 with the membrane are crucial for the toxicity. The cutoff alcohols, C₁₄ and C₁₆, also exhibited strong toxicity obeying the Meyer-Overton correlation, in intact yeast cells whose membrane growth was suppressed in water. Taken together, the cutoff is avoidable by securing sufficient accumulation of the wall-permeable monomers in the membrane, which supports the lipid theory. It would be important to distinguish the effective drug structure localizing in the membrane and deal with the amount in the membrane.

AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods—even if high throughput—are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful—experimentally verified—associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.

Machine Learning Applications for Chemical Reactions

Machine learning (ML) approaches have enabled rapid and efficient molecular property predictions as well as the design of new novel materials. In addition to great success for molecular problems, ML techniques are applied to various chemical reaction problems that require huge costs to solve with the existing experimental and simulation methods. In this review, starting with basic representations of chemical reactions, we summarized recent achievements of ML studies on two different problems; predicting reaction properties and synthetic routes. The various ML models are used to predict physical properties related to chemical reaction properties (e. g. thermodynamic changes, activation barriers, and reaction rates). Furthermore, the predictions of reactivity, self-optimization of reaction, and designing retrosynthetic reaction paths are also tackled by ML approaches. Herein we illustrate various ML strategies utilized in the various context of chemical reaction studies.

Computational Methods in Cooperation with Experimental Approaches to Design Protein Tyrosine Phosphatase 1B Inhibitors in Type 2 Diabetes Drug Design: A Review of the Achievements of This Century

Protein tyrosine phosphatase 1B (PTP1B) dephosphorylates phosphotyrosine residues and is an important regulator of several signaling pathways, such as insulin, leptin, and the ErbB signaling network, among others. Therefore, this enzyme is considered an attractive target to design new drugs against type 2 diabetes, obesity, and cancer. To date, a wide variety of PTP1B inhibitors that have been developed by experimental and computational approaches. In this review, we summarize the achievements with respect to PTP1B inhibitors discovered by applying computer-assisted drug design methodologies (virtual screening, molecular docking, pharmacophore modeling, and quantitative structure–activity relationships (QSAR)) as the principal strategy, in cooperation with experimental approaches, covering articles published from the beginning of the century until the time this review was submitted, with a focus on studies conducted with the aim of discovering new drugs against type 2 diabetes. This review encourages the use of computational techniques and includes helpful information that increases the knowledge generated to date about PTP1B inhibition, with a positive impact on the route toward obtaining a new drug against type 2 diabetes with PTP1B as a molecular target.

Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents

The COVID-19 pandemic has had enormous health, economic, and social consequences. Vaccines have been successful in reducing rates of infection and hospitalization, but there is still a need for acute treatment of the disease. We investigate whether compounds that bind the human angiotensin-converting enzyme 2 (ACE2) protein can decrease SARS-CoV-2 replication without impacting ACE2's natural enzymatic function. Initial screening of a diversity library resulted in hit compounds active in an ACE2-binding assay, which showed little inhibition of ACE2 enzymatic activity (116 actives, success rate ∼4%), suggesting they were allosteric binders. Subsequent application of in silico techniques boosted success rates to ∼14% and resulted in 73 novel confirmed ACE2 binders with K d values as low as 6 nM. A subsequent SARS-CoV-2 assay revealed that five of these compounds inhibit the viral life cycle in human cells. Further effort is required to completely elucidate the antiviral mechanism of these ACE2-binders, but they present a valuable starting point for both the development of acute treatments for COVID-19 and research into the host-directed therapy.

Molecular field analysis for data-driven molecular design in asymmetric catalysis

This review highlights the recent advances (2019-present) in the use of MFA (molecular field analysis) for data-driven catalyst design, enabling to improve selectivities/reaction outcomes in asymmetric catalysis. Successful examples of MFA-based molecular design and how to design molecules by MFA are described, including how to generate and evaluate MFA-based regression models, and future challenges in MFA-based molecular design in molecular catalysis.

Perceiving the Concealed and Unreported Pharmacophoric Features of the 5-Hydroxytryptamine Receptor Using Balanced QSAR Analysis

The 5-hydroxytryptamine receptor 6 (5-HT6) has gained attention as a target for developing therapeutics for Alzheimer’s disease, schizophrenia, cognitive dysfunctions, anxiety, and depression, to list a few. In the present analysis, a larger and diverse dataset of 1278 molecules covering a broad chemical and activity space was used to identify visual and concealed structural features associated with binding affinity for 5-HT6. For this, quantitative structure–activity relationships (QSAR) and molecular docking analyses were executed. This led to the development of a statistically robust QSAR model with a balance of excellent predictivity (R²_tr = 0.78, R²_ex = 0.77), the identification of unreported aspects of known features, and also novel mechanistic interpretations. Molecular docking and QSAR provided similar as well as complementary results. The present analysis indicates that the partial charges on ring carbons present within four bonds from a sulfur atom, the occurrence of sp3-hybridized carbon atoms bonded with donor atoms, and a conditional occurrence of lipophilic atoms/groups from nitrogen atoms, which are prominent but unreported pharmacophores that should be considered while optimizing a molecule for 5-HT6. Thus, the present analysis led to identification of some novel unreported structural features that govern the binding affinity of a molecule. The results could be beneficial in optimizing the molecules for 5-HT6.

Application of Machine Learning in Developing Quantitative Structure-Property Relationship for Electronic Properties of Polyaromatic Compounds

The degree of π orbital overlap (DPO) model has been demonstrated to be an excellent quantitative structure-property relationship (QSPR) that can map two-dimensional structural information of polycyclic aromatic hydrocarbons (PAHs) and thienoacenes to their electronic properties, namely, band gaps, electron affinities, and ionization potentials. However, the model suffers from significant limitations that narrow its applications due to inefficient manual procedures in parameter optimization and descriptor formulation. In this work, we developed a machine learning (ML)-based method for efficiently optimizing DPO parameters and proposed a truncated DPO descriptor, which is simple enough that can be automatically extracted from simplified molecular-input line-entry system strings of PAHs and thienoacenes. Compared with the result from our previous studies, the ML-based methodology can optimize DPO parameters with four times fewer data, while it can achieve the same level of accuracy in predictions of the mentioned electronic properties to within 0.1 eV. The truncated DPO model also has similar accuracy to the full DPO model. Consequently, the ML-based DPO approach coupled with the truncated DPO model enables new possibilities for developing automatic pipelines for high-throughput screening and investigating new QSPR for new chemical classes.

First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability

Quantitative structure-activity relationship (QSAR) and read-across techniques have recently been merged into a new emerging field of read-across structure-activity relationship (RASAR) that uses the chemical similarity concepts of read-across (an unsupervised step) and finally develops a supervised learning model (like QSAR). The RASAR method has so far been used only in case of graded predictions or classification modeling. In this work, we attempt, for the first time, to apply RASAR for quantitative predictions (q-RASAR) using a case study of androgen receptor binding affinity data. We have computed a number of error-based and similarity-based measures such as weighted standard deviation of the predicted values, coefficient of variation of the computed predictions, average similarity level of close training compounds for each query molecule, standard deviation and coefficient of variation of similarity levels, maximum similarity levels to positive and negative close training compounds, a concordance measure indicating similarity to positive, negative or both classes of close training compounds, etc. We have clubbed these additional measures along with the selected chemical descriptors from the previously developed QSAR model and redeveloped new partial least squares models from the training set, and predicted the endpoint using the query data set. Interestingly, these new models outperform the internal and external validation quality of the original QSAR model. In this study, we have also introduced a new similarity-based concordance measure (Banerjee-Roy coefficient) that can significantly contribute to the model quality. A q-RASAR model also has the advantage over read-across predictions in providing easy interpretation and indicating quantitative contributions of important chemical features. The strategy described here should be applicable to other biological/toxicological/property data modeling for enhanced quality of predictions, easy interpretability, and efficient transferability.

First Novel Workflow for Semiquantification of Emerging Contaminants in Environmental Samples Analyzed by Gas Chromatography-Atmospheric Pressure Chemical Ionization-Quadrupole Time of Flight-Mass Spectrometry

The ionization efficiency of emerging contaminants was modeled for the first time in gas chromatography-high-resolution mass spectrometry (GC-HRMS) which is coupled to an atmospheric pressure chemical ionization source (APCI). The recent chemical space has been expanded in environmental samples such as soil, indoor dust, and sediments thanks to recent use of high-resolution mass spectrometric techniques; however, many of these chemicals have remained unquantified. Chemical exposure in dust can pose potential risk to human health, and semiquantitative analysis is potentially of need to semiquantify these newly identified substances and assist with their risk assessment and environmental fate. In this study, a rigorously tested semiquantification workflow was proposed based on GC-APCI-HRMS ionization efficiency measurements of 78 emerging contaminants. The mechanism of ionization of compounds in the APCI source was discussed via a simple connectivity index and topological structure. The quantitative structure–property relationship (QSPR)-based model was also built to predict the APCI ionization efficiencies of unknowns and later use it for their quantification analyses. The proposed semiquantification method could be transferred into the household indoor dust sample matrix, and it could include the effect of recovery and matrix in the predictions of actual concentrations of analytes. A suspect compound, which falls inside the application domain of the tool, can be semiquantified by an online web application, free of access at http://trams.chem.uoa.gr/semiquantification/.

Microbiological Aspects of Unique, Rare, and Unusual Fatty Acids Derived from Natural Amides and Their Pharmacological Profile

In the proposed review, the pharmacological profile of unique, rare, and unusual fatty acids derived from natural amides is considered. These amides are produced by various microorganisms, lichens, and fungi. The biological activity of some natural fatty acid amides has been determined by their isolation from natural sources, but the biological activity of fatty acids has not been practically studied. According to QSAR data, the biological activity of fatty acids is shown, which demonstrated strong antifungal, antibacterial, antiviral, antineoplastic, anti-inflammatory activities. Moreover, some fatty acids have shown rare activities such as antidiabetic, anti-infective, anti-eczematic, antimutagenic, and anti-psoriatic activities. For some fatty acids that have pronounced biological properties, 3D graphs are shown that show a graphical representation of unique activities. These data are undoubtedly of both theoretical and practical interest for chemists, pharmacologists, as well as for the pharmaceutical industry, which is engaged in the synthesis of biologically active drugs.

Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks

Assessing whether compounds penetrate the brain can become critical in drug discovery, either to prevent adverse events or to reach the biological target. Generally, pre-clinical in vivo studies measuring the ratio of brain and blood concentrations (K_p) are required to estimate the brain penetration potential of a new drug entity. In this work, we developed machine learning models to predict in vivo compound brain penetration (as LogK_p) from chemical structure. Our results show the benefit of including in vitro experimental data as auxiliary tasks in multi-task graph neural network (MT-GNN) models. MT-GNNs outperformed single-task (ST) models solely trained on in vivo brain penetration data. The best-performing MT-GNN regression model achieved a coefficient of determination of 0.42 and a mean absolute error of 0.39 (2.5-fold) on a prospective validation set and outperformed all tested ST models. To facilitate decision-making, compounds were classified into brain-penetrant or non-penetrant, achieving a Matthew's correlation coefficient of 0.66. Taken together, our findings indicate that the inclusion of in vitro assay data as MT-GNN auxiliary tasks improves in vivo brain penetration predictions and prospective compound prioritization.

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure-activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.

Mechanistic Analysis of Chemically Diverse Bromodomain-4 Inhibitors Using Balanced QSAR Analysis and Supported by X-ray Resolved Crystal Structures

Bromodomain-4 (BRD-4) is a key enzyme in post-translational modifications, transcriptional activation, and many other cellular processes. Its inhibitors find their therapeutic usage in cancer, acute heart failure, and inflammation to name a few. In the present study, a dataset of 980 molecules with a significant diversity of structural scaffolds and composition was selected to develop a balanced QSAR model possessing high predictive capability and mechanistic interpretation. The model was built as per the OECD (Organisation for Economic Co-operation and Development) guidelines and fulfills the endorsed threshold values for different validation parameters (R²_tr = 0.76, Q²_LMO = 0.76, and R²_ex = 0.76). The present QSAR analysis identified that anti-BRD-4 activity is associated with structural characters such as the presence of saturated carbocyclic rings, the occurrence of carbon atoms near the center of mass of a molecule, and a specific combination of planer or aromatic nitrogen with ring carbon, donor, and acceptor atoms. The outcomes of the present analysis are also supported by X-ray-resolved crystal structures of compounds with BRD-4. Thus, the QSAR model effectively captured salient as well as unreported hidden pharmacophoric features. Therefore, the present study successfully identified valuable novel pharmacophoric features, which could be beneficial for the future optimization of lead/hit compounds for anti-BRD-4 activity.

Machine learning hybrid approach for the prediction of surface tension profiles of hydrocarbon surfactants in aqueous solution

HYPOTHESIS

Predicting the surface tension (SFT)-log(c) profiles of hydrocarbon surfactants in aqueous solution is computationally non-trivial, and empirically challenging due to the diverse and complex architecture and interactions of surfactant molecules. Machine learning (ML), combining a data-based and knowledge-based approach, can provide a powerful means to relate molecular descriptors to SFT profiles.

EXPERIMENTS

A dataset of SFT for 154 model hydrocarbon surfactants at 20-30 °C is fitted to the Szyszkowski equation to extract three characteristic parameters (Γ_max,K_L and critical micelle concentration (CMC)) which are correlated to a series of 2D and 3D molecular descriptors. Key (∼10) descriptors were selected by removing co-correlation, and employing a gradient-boosted regressor model to rank feature importance and carry out recursive feature elimination (RFE). The hyperparameters of each target-variable model were fine-tuned using a randomised cross-validated grid search, to improve predictive ability and reduce overfitting.

FINDINGS

The ML models correlate favourably with test experimental data, with R²= 0.69-0.87, and the merits and limitations of the approach are discussed based on 'unseen' hydrocarbon surfactants. The incorporation of a knowledge-based framework provides an appropriate smoothing of the experimental data which simplifies the data-driven approach and enhances its generality. Open-source codes and a brief tutorial are provided.

Natural Products from Annonaceae as Potential Antichagasic Agents

Chagas disease, a neglected tropical disease, is endemic in 21 Latin American countries and particularly prevalent in Brazil. Chagas disease has drawn more attention in recent years due to its expansion into non-endemic areas. The aim of this work was to computationally identify and experimentally validate the natural products from an Annonaceae family as antichagasic agents. Through the ligand-based virtual screening, we identified 57 molecules with potential activity against the epimastigote form of T. cruzi. Then, 16 molecules were analyzed in the in vitro study, of which, six molecules displayed previously unknown antiepimastigote activity. We also evaluated these six molecules for trypanocidal activity. We observed that all six molecules have potential activity against the amastigote form, but no molecules were active against the trypomastigote form. 13-Epicupressic acid seems to be the most promising, as it was predicted as an active compound in the in silico study against the amastigote form of T. cruzi, in addition to having in vitro activity against the epimastigote form.

In-silico and in-vitro studies on the efficacy of mangiferin against colorectal cancer

Background

Mangiferin is a C-glycoside xanthone molecule having a wide range of therapeutic properties. Hence, the present study aims to understand the efficacy of mangiferin against colorectal cancer (CRC) and to elucidate the mechanisms of action of mangiferin on colorectal cancer.

Method

The molecular mechanism of mangiferin against colorectal cancer was studied using Autodock Vina software. Pharmacophore analysis of mangiferin concerning five COX-2 inhibitor drugs was carried out using the PharmaGist server to analyze the possibility of using mangiferin as a COX-2 inhibitor. In vitro analysis of Mangiferin against various cancer cell lines was performed.

Results

The molecular mechanism of action of mangiferin against CRC was assessed by docking with multiple target proteins involved in the progression of CRC. Docking studies showed good binding scores (kcal/mol) ranging from − 10.3 to − 6.7. Mangiferin showed a good affinity towards enzymes like COX-2 and LA4H involved in Arachidonic acid (AA) metabolism with a binding score(kcal/mol) of − 10.1 and − 10.3 respectively. The pharmacophore feature assessment of mangiferin was done for COX-2 inhibitor drugs, which further confirmed that mangiferin poses the same pharmacophore feature as that of COX-2 inhibitor drugs. Furthermore, the binding affinity of mangiferin was compared with five COX-2 inhibitor drugs to prove its efficacy as an inhibitor. Mangiferin also had a cytotoxic effect against colorectal cancer (HT 29), cervical cancer (HeLa), and breast cancer (MCF 7) cell lines. The study could establish that Mangiferin might be a promising candidate for the treatment of colorectal cancer.

Conclusion

In short, these studies exploited the possibility of mangiferin as a lead molecule to develop anticancer/anti-inflammatory drugs for the treatment of CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13065-022-00835-9.

Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction

Recently, imputation techniques have been adapted to predict activity values among sparse bioactivity matrices, showing improvements in predictive performance over traditional QSAR models. These models are able to use experimental activity values for auxiliary assays when predicting the activity of a test compound on a specific assay. In this study, we tested three different multi-task imputation techniques on three classification-based toxicity datasets: two of small scale (12 assays each) and one large scale with 417 assays. Moreover, we analyzed in detail the improvements shown by the imputation models. We found that test compounds that were dissimilar to training compounds, as well as test compounds with a large number of experimental values for other assays, showed the largest improvements. We also investigated the impact of sparsity on the improvements seen as well as the relatedness of the assays being considered. Our results show that even a small amount of additional information can provide imputation methods with a strong boost in predictive performance over traditional single task and multi-task predictive models.

Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling

Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.

Quantitative Structure-Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure

The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSARs). Herein, we develop QSAR models that quantitatively predict both thermodynamic- and kinetic-based binding parameters of small molecules and the HIV-1 transactivation response (TAR) RNA model system. Small molecules bearing diverse scaffolds were screened against TAR using surface plasmon resonance. Multiple linear regression (MLR) combined with feature selection afforded robust models that allowed direct interpretation of the properties critical for both binding strength and kinetic rate constants. These models were validated with new molecules, and their accurate performance was confirmed via comparison to ensemble tree methods, supporting the general applicability of this platform.

Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function

DNA-encoded library (DEL) screening and quantitative structure-activity relationship (QSAR) modeling are two techniques used in drug discovery to find novel small molecules that bind a protein target. Applying QSAR modeling to DEL selection data can facilitate the selection of compounds for off-DNA synthesis and evaluation. Such a combined approach has been done recently by training binary classifiers to learn DEL enrichments of aggregated "disynthons" in order to accommodate the sparse and noisy nature of DEL data. However, a binary classification model cannot distinguish between different levels of enrichment, and information is potentially lost during disynthon aggregation. Here, we demonstrate a regression approach to learning DEL enrichments of individual molecules, using a custom negative-log-likelihood loss function that effectively denoises DEL data and introduces opportunities for visualization of learned structure-activity relationships. Our approach explicitly models the Poisson statistics of the sequencing process used in the DEL experimental workflow under a frequentist view. We illustrate this approach on a DEL dataset of 108,528 compounds screened against carbonic anhydrase (CAIX), and a dataset of 5,655,000 compounds screened against soluble epoxide hydrolase (sEH) and SIRT2. Due to the treatment of uncertainty in the data through the negative-log-likelihood loss used during training, the models can ignore low-confidence outliers. While our approach does not demonstrate a benefit for extrapolation to novel structures, we expect our denoising and visualization pipeline to be useful in identifying structure-activity trends and highly enriched pharmacophores in DEL data. Further, this approach to uncertainty-aware regression modeling is applicable to other sparse or noisy datasets where the nature of stochasticity is known or can be modeled; in particular, the Poisson enrichment ratio metric we use can apply to other settings that compare sequencing count data between two experimental conditions.

An Overview of Antiviral Peptides and Rational Biodesign Considerations

Viral diseases have contributed significantly to worldwide morbidity and mortality throughout history. Despite the existence of therapeutic treatments for many viral infections, antiviral resistance and the threat posed by novel viruses highlight the need for an increased number of effective therapeutics. In addition to small molecule drugs and biologics, antimicrobial peptides (AMPs) represent an emerging class of potential antiviral therapeutics. While AMPs have traditionally been regarded in the context of their antibacterial activities, many AMPs are now known to be antiviral. These antiviral peptides (AVPs) have been shown to target and perturb viral membrane envelopes and inhibit various stages of the viral life cycle, from preattachment inhibition through viral release from infected host cells. Rational design of AMPs has also proven effective in identifying highly active and specific peptides and can aid in the discovery of lead peptides with high therapeutic selectivity. In this review, we highlight AVPs with strong antiviral activity largely curated from a publicly available AMP database. We then compile the sequences present in our AVP database to generate structural predictions of generic AVP motifs. Finally, we cover the rational design approaches available for AVPs taking into account approaches currently used for the rational design of AMPs.

In silico models to predict tubular secretion or reabsorption clearance pathway using physicochemical properties and structural characteristics

1. Renal clearance is one of the main pathways for a drug to be cleared from plasma. The aim of this study is to develop in-silico models to find out the relationship between the type of renal clearance, and structural parameters.2. Literature data were used to categorize the drugs into those that undergo tubular secretion and those that undergo reabsorption. Different structural descriptors (VolSurf descriptors, Abraham solvation parameters, data warrior descriptors, logarithm of distribution coefficient at pH =7.4 (logD7.4)) were applied to develop a mechanistic model for estimating renal clearance class whether its secretion or reabsorption.3. The results of this study show that logD7.4, and the number of hydrogen bond donors as well as available uncharged species (AUS7.4) are the most effective descriptors to establish mechanistic models for predicting renal clearance class. The classification models were established with a level of accuracy of more than 75%.4. Developed models of this study can be helpful to predict renal clearance class for new drug candidates with an acceptable error. Hydrophilicity and hydrogen bond formation ability of drugs are among the main descriptors.

Accelerating AutoDock Vina with GPUs

AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.

Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data

For hazard identification, classification, and labeling purposes, animal testing guidelines are required by law to evaluate the developmental toxicity potential of new and existing chemical products. However, guideline developmental toxicity studies are costly, time-consuming, and require many laboratory animals. Computational modeling has emerged as a promising, animal-sparing, and cost-effective method for evaluating the developmental toxicity potential of chemicals, such as endocrine disruptors, without the use of animals. We aimed to develop a predictive and explainable computational model for developmental toxicants. To this end, a comprehensive dataset of 1244 chemicals with developmental toxicity classifications was curated from public repositories and literature sources. Data from 2140 toxicological high-throughput screening assays were extracted from PubChem and the ToxCast program for this dataset and combined with information about 834 chemical fragments to group assays based on their chemical-mechanistic relationships. This effort revealed two assay clusters containing 83 and 76 assays, respectively, with high positive predictive rates for developmental toxicants identified with animal testing guidelines (PPV = 72.4 and 77.3% during cross-validation). These two assay clusters can be used as developmental toxicity models and were applied to predict new chemicals for external validation. This study provides a new strategy for constructing alternative chemical developmental toxicity evaluations that can be replicated for other toxicity modeling studies.

Predicting chemical hazard across taxa through machine learning

We applied machine learning methods to predict chemical hazards focusing on fish acute toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup, showing that taking them into account can lead to considerable improvements in the classification performance. We quantified the gain obtained throught the introduction of taxonomic and experimental information, compared to classification based on chemical information alone. We used our approach with standard machine learning models (K-nearest neighbors, random forests and deep neural networks), as well as the recently proposed Read-Across Structure Activity Relationship (RASAR) models, which were very successful in predicting chemical hazards to mammals based on chemical similarity. We were able to obtain accuracies of over 93% on datasets where, due to noise in the data, the maximum achievable accuracy was expected to be below 96%. The best performances were obtained by random forests and RASAR models. We analyzed metrics to compare our results with animal test reproducibility, and despite most of our models "outperform animal test reproducibility" as measured through recently proposed metrics, we showed that the comparison between machine learning performance and animal test reproducibility should be addressed with particular care. While we focused on fish mortality, our approach, provided that the right data is available, is valid for any combination of chemicals, effects and taxa.

Predicting chemical ecotoxicity by learning latent space chemical representations

In silico prediction of chemical ecotoxicity (HC₅₀) represents an important complement to improve in vivo and in vitro toxicological assessment of manufactured chemicals. Recent application of machine learning models to predict chemical HC₅₀ yields variable prediction performance that depends on effectively learning chemical representations from high-dimension data. To improve HC₅₀ prediction performance, we developed an autoencoder model by learning latent space chemical embeddings. This novel approach achieved state-of-the-art prediction performance of HC₅₀ with R² of 0.668 ± 0.003 and mean absolute error (MAE) of 0.572 ± 0.001, and outperformed other dimension reduction methods including principal component analysis (PCA) (R² = 0.601 ± 0.031 and MAE = 0.629 ± 0.005), kernel PCA (R² = 0.631 ± 0.008 and MAE = 0.625 ± 0.006), and uniform manifold approximation and projection dimensionality reduction (R² = 0.400 ± 0.008 and MAE = 0.801 ± 0.002). A simple linear layer with chemical embeddings learned from the autoencoder model performed better than random forest (R² = 0.663 ± 0.007 and MAE = 0.591 ± 0.008), fully connected neural network (R² = 0.614 ± 0.016 and MAE = 0.610 ± 0.008), least absolute shrinkage and selection operator (R² = 0.617 ± 0.037 and MAE = 0.619 ± 0.007), and ridge regression (R² = 0.638 ± 0.007 and MAE = 0.613 ± 0.005) using unlearned raw input features. Our results highlighted the usefulness of learning latent chemical representations, and our autoencoder model provides an alternative approach for robust HC₅₀ prediction.

In Silico Prediction of Skin Permeability Using a Two-QSAR Approach

Topical and transdermal drug delivery is an effective, safe, and preferred route of drug administration. As such, skin permeability is one of the critical parameters that should be taken into consideration in the process of drug discovery and development. The ex vivo human skin model is considered as the best surrogate to evaluate in vivo skin permeability. This investigation adopted a novel two-QSAR scheme by collectively incorporating machine learning-based hierarchical support vector regression (HSVR) and classical partial least square (PLS) to predict the skin permeability coefficient and to uncover the intrinsic permeation mechanism, respectively, based on ex vivo excised human skin permeability data compiled from the literature. The derived HSVR model functioned better than PLS as represented by the predictive performance in the training set, test set, and outlier set in addition to various statistical estimations. HSVR also delivered consistent performance upon the application of a mock test, which purposely mimicked the real challenges. PLS, contrarily, uncovered the interpretable relevance between selected descriptors and skin permeability. Thus, the synergy between interpretable PLS and predictive HSVR models can be of great use for facilitating drug discovery and development by predicting skin permeability.

Natural Polyether Ionophores and Their Pharmacological Profile

This review is devoted to the study of the biological activity of polyether ionophores produced by bacteria, unicellular marine algae, red seaweeds, marine sponges, and coelenterates. Biological activities have been studied experimentally in various laboratories, as well as data obtained using QSAR (Quantitative Structure-Activity Relationships) algorithms. According to the data obtained, it was shown that polyether toxins exhibit strong antibacterial, antimicrobial, antifungal, antitumor, and other activities. Along with this, it was found that natural polyether ionophores exhibit such properties as antiparasitic, antiprotozoal, cytostatic, anti-mycoplasmal, and antieczema activities. In addition, polyethers have been found to be potential regulators of lipid metabolism or inhibitors of DNA synthesis. Further study of the mechanisms of action and the search for new polyether ionophores and their derivatives may provide more effective therapeutic natural polyether ionophores for the treatment of cancer and other diseases. For some polyether ionophores, 3D graphs are presented, which demonstrate the predicted and calculated activities. The data presented in this review will be of interest to pharmacologists, chemists, practical medicine, and the pharmaceutical industry.

A novel workflow for semi-quantification of emerging contaminants in environmental samples analyzed by LC-HRMS

There is an increasing need for developing a strategy to quantify the newly identified substances in environmental samples, where there are not always reference standards available. The semi-quantitative analysis can assist risk assessment of chemicals and their environmental fate. In this study, a rigorously tested and system-independent semi-quantification workflow is proposed based on ionization efficiency measurement of emerging contaminants analyzed in liquid chromatography-high-resolution mass spectrometry. The quantitative structure-property relationship (QSPR)-based model was built to predict the ionization efficiency of unknown compounds which can be later used for their semi-quantification. The proposed semi-quantification method was applied and tested in real environmental seawater samples. All semi-quantification-related calculations can be performed online and free of access at http://trams.chem.uoa.gr/semiquantification/ .

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.

Machine Learning in Chemoinformatics and Medicinal Chemistry

In chemoinformatics and medicinal chemistry, machine learning has evolved into an important approach. In recent years, increasing computational resources and new deep learning algorithms have put machine learning onto a new level, addressing previously unmet challenges in pharmaceutical research. In silico approaches for compound activity predictions, de novo design, and reaction modeling have been further advanced by new algorithmic developments and the emergence of big data in the field. Herein, novel applications of machine learning and deep learning in chemoinformatics and medicinal chemistry are reviewed. Opportunities and challenges for new methods and applications are discussed, placing emphasis on proper baseline comparisons, robust validation methodologies, and new applicability domains. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Computer Aided Drug Design Methodologies with Natural Products in the Drug Research Against Alzheimer's Disease

Natural products are compounds isolated from plants that provide a variety of lead structures for the development of new drugs by the pharmaceutical industry. The interest in these substances increases because of their beneficial effects on human health. Alzheimer's disease (AD) affects occur in about 80% of individuals aged 65 years. AD, the most common cause of dementia in elderly people, is characterized by progressive neurodegenerative alterations, as decrease of cholinergic impulse, increased toxic effects caused by reactive oxygen species and the inflammatory process that the amyloid plaque participates. In silico studies is relevant in the process of drug discovery; through technological advances in the areas of structural characterization of molecules, computational science and molecular biology have contributed to the planning of new drugs used against neurodegenerative diseases. Considering the social impairment caused by an increased incidence of disease and that there is no chemotherapy treatment effective against AD; several compounds are studied. In the researches for effective neuroprotectants as potential treatments for Alzheimer's disease, natural products have been extensively studied in various AD models. This study aims to carry out a literature review with articles that address the in silico studies of natural products aimed at potential drugs against Alzheimer's disease (AD) in the period from 2015 to 2021.

Investigation of potential descriptors of chemical compounds on prevention of nephrotoxicity via QSAR approach

Drug-induced nephrotoxicity remains a common problem after exposure to medications and diagnostic agents, which may be heightened in the kidney microenvironment and deteriorate kidney function. In this study, the toxic effects of fourteen marked drugs with the individual chemical structure were evaluated in kidney cells. The quantitative structure-activity relationship (QSAR) approach was employed to investigate the potential structural descriptors of each drug-related to their toxic effects. The most reasonable equation of the QSAR model displayed that the estimated regression coefficients such as the number of ring assemblies, three-membered rings, and six-membered rings were strongly related to toxic effects on renal cells. Meanwhile, the chemical properties of the tested compounds including carbon atoms, bridge bonds, H-bond donors, negative atoms, and rotatable bonds were favored properties and promote the toxic effects on renal cells. Particularly, more numbers of rotatable bonds were positively correlated with strong toxic effects that displayed on the most toxic compound. The useful information discovered from our regression QSAR models may help to identify potential hazardous moiety to avoid nephrotoxicity in renal preventive medicine.

QSAR analysis on a large and diverse set of potent phosphoinositide 3-kinase gamma (PI3Kγ) inhibitors using MLR and ANN methods

Phosphorylation of PI3Kγ as a member of lipid kinases-enzymes, plays a crucial role in regulating immune cells through the generation of intracellular signals. Deregulation of this pathway is involved in several tumors. In this research, diverse sets of potent and selective isoform-specific PI3Kγ inhibitors whose drug-likeness was confirmed based on Lipinski’s rule of five were used in the modeling process. Genetic algorithm (GA)-based multivariate analysis was employed on the half-maximal inhibitory concentration (IC₅₀) of them. In this way, multiple linear regression (MLR) and artificial neural network (ANN) algorithm, were used to QSAR models construction on 245 compounds with a wide range of pIC₅₀ (5.23–9.32). The stability and robustness of the models have been evaluated by external and internal validation methods (R² 0.623–0.642, RMSE 0.464–0.473, F 40.114, Q²_LOO 0.600, and R²_y-random 0.011). External verification using a wide variety of structures out of the training and test sets show that ANN is superior to MLR. The descriptors entered into the model are in good agreement with the X-ray structures of target-ligand complexes; so the model is interpretable. Finally, Williams plot-based analysis was applied to simultaneously compare the inhibitory activity and structural similarity of training, test and validation sets.

Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency

Under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) analysis of alternatives (AoA) process, quantitative structure–activity relationship (QSAR) models play an important role in expanding information gathering and organizing frameworks. Increasingly recognized as an alternative to testing under registration. QSARs have become a relevant tool in bridging data gaps and supporting weight of evidence (WoE) when assessing alternative substances. Additionally, QSARs are growing in importance in integrated testing strategies (ITS). For example, the REACH ITS framework for specific endpoints directs registrants to consider non-testing results, including QSAR predictions, when deciding if further animal testing is needed. Despite the raised profile of QSARs in these frameworks, a gap exists in the evaluation of QSAR use and QSAR documentation under authorization. An assessment of the different uses (e.g., WoE and ITS) in which QSAR predictions play a role in evidence gathering and organizing remains unaddressed for AoA. This study approached the disparity in information for QSAR predictions by conducting a substantive review of 24 AoA through May 2017, which contained higher-tier endpoints under REACH. Understanding the manner in which applicants manage QSAR prediction information in AoA and assessing their potential within ITS will be valuable in promoting regulatory use of QSARs and building out future platforms in the face of rapidly evolving technology while advancing information transparency.