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1.
Bioorg Med Chem ; 46: 116388, 2021 Sep 15.
Artigo em Inglês | MEDLINE | ID: mdl-34488021

RESUMO

The vast majority of approved drugs are metabolized by the five major cytochrome P450 (CYP) isozymes, 1A2, 2C9, 2C19, 2D6 and 3A4. Inhibition of CYP isozymes can cause drug-drug interactions with severe pharmacological and toxicological consequences. Computational methods for the fast and reliable prediction of the inhibition of CYP isozymes by small molecules are therefore of high interest and relevance to pharmaceutical companies and a host of other industries, including the cosmetics and agrochemical industries. Today, a large number of machine learning models for predicting the inhibition of the major CYP isozymes by small molecules are available. With this work we aim to go beyond the coverage of existing models, by combining data from several major public and proprietary sources. More specifically, we used up to 18815 compounds with measured bioactivities to train random forest classification models for the individual CYP isozymes. A major advantage of the new data collection over existing ones is the better representation of the minority class, the CYP inhibitors. With the new data collection we achieved inhibitor-to-non-inhibitor ratios in the order of 1:1 (CYP1A2) to 1:3 (CYP2D6). We show that our models reach competitive performance on external data, with Matthews correlation coefficients (MCCs) ranging from 0.62 (CYP2C19) to 0.70 (CYP2D6), and areas under the receiver operating characteristic curve (AUCs) between 0.89 (CYP2C19) and 0.92 (CYPs 2D6 and 3A4). Importantly, the models show a high level of robustness, reflected in a good predictivity also for compounds that are structurally dissimilar to the compounds represented in the training data. The best models presented in this work are freely accessible for academic research via a web service.

2.
Pharmaceuticals (Basel) ; 14(8)2021 Aug 11.
Artigo em Inglês | MEDLINE | ID: mdl-34451887

RESUMO

In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model ("Skin Doctor CP:Bio") obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds.

3.
Molecules ; 26(15)2021 Aug 02.
Artigo em Inglês | MEDLINE | ID: mdl-34361831

RESUMO

The interaction of small organic molecules such as drugs, agrochemicals, and cosmetics with cytochrome P450 enzymes (CYPs) can lead to substantial changes in the bioavailability of active substances and hence consequences with respect to pharmacological efficacy and toxicity. Therefore, efficient means of predicting the interactions of small organic molecules with CYPs are of high importance to a host of different industries. In this work, we present a new set of machine learning models for the classification of xenobiotics into substrates and non-substrates of nine human CYP isozymes: CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4. The models are trained on an extended, high-quality collection of known substrates and non-substrates and have been subjected to thorough validation. Our results show that the models yield competitive performance and are favorable for the detection of CYP substrates. In particular, a new consensus model reached high performance, with Matthews correlation coefficients (MCCs) between 0.45 (CYP2C8) and 0.85 (CYP3A4), although at the cost of coverage. The best models presented in this work are accessible free of charge via the "CYPstrate" module of the New E-Resource for Drug Discovery (NERDD).


Assuntos
Sistema Enzimático do Citocromo P-450/metabolismo , Aprendizado de Máquina , Xenobióticos/classificação , Xenobióticos/metabolismo , Animais , Humanos , Especificidade por Substrato
4.
Int J Mol Sci ; 22(15)2021 Jul 21.
Artigo em Inglês | MEDLINE | ID: mdl-34360558

RESUMO

Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the "fitness" of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle ("BonMOLière").


Assuntos
Algoritmos , Descoberta de Drogas , Ensaios de Triagem em Larga Escala/normas , Proteínas/química , Bibliotecas de Moléculas Pequenas/química , Bibliotecas de Moléculas Pequenas/farmacologia , Avaliação Pré-Clínica de Medicamentos , Ensaios de Triagem em Larga Escala/métodos , Humanos
5.
Biosci Rep ; 41(7)2021 Jul 30.
Artigo em Inglês | MEDLINE | ID: mdl-34232294

RESUMO

Overexpression of the neuronal InsP3kinase-A increases malignancy of different tumor types. Since InsP3kinase-A highly selectively binds Ins(1,4,5)P3, small molecules competing with Ins(1,4,5)P3 provide a promising approach for the therapeutic targeting of InsP3kinase-A. Based on this consideration, we analyzed the binding mechanism of BIP-4 (2-[3,5-dimethyl-1-(4-nitrophenyl)-1H-pyrazol-4-yl]-5, 8-dinitro-1H-benzo[de]isoquinoline-1,3(2H)-dione), a known competitive small-molecule inhibitor of Ins(1,4,5)P3. We tested a total of 80 BIP-4 related compounds in biochemical assays. The results of these experiments revealed that neither the nitrophenyl nor the benzisochinoline group inhibited InsP3kinase-A activity. Moreover, none of the BIP-4 related compounds competed for Ins(1,4,5)P3, demonstrating the high selectivity of BIP-4. To analyze the inhibition mechanism of BIP-4, mutagenesis experiments were performed. The results of these experiments suggest that the nitro groups attached to the benzisochinoline ring compete for binding of Ins(1,4,5)P3 while the nitrophenyl group is associated with amino acids of the ATP-binding pocket. Our results now offer the possibility to optimize BIP-4 to design specific InsP3Kinase-A inhibitors suitable for therapeutic targeting of the enzyme.

6.
Biochem Biophys Res Commun ; 568: 110-115, 2021 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-34214875

RESUMO

The phosphoinositides phosphatidylinositol-3,4,5-trisphosphate [PtdIns(3,4,5)P3] and phosphatidylinositol-3,4-bisphosphate [PtdIns(3,4)P2] function as second messengers and have been implicated in cancerogenesis. The signalling events downstream of PtdIns(3,4,5)P3 and PtdIns(3,4)P2 are mediated through a complex network of phosphoinositide binding effector proteins and phosphatases. In this study, we compared the phosphoinositide effector proteins AKT1, TAPP1, TAPP2, VAV1 and P-REX1 and the phosphoinositide phosphatases PTEN, SHIP1 and INPP4B for their binding affinities to PtdIns(3,4,5)P3 and/or PtdIns(3,4)P2 using Surface Plasmon Resonance. Our results demonstrate that all measured proteins except P-REX1 and VAV1 showed high affinity phosphoinositide binding with KD values in the nM to sub-nM range. Within the effector proteins, AKT1 showed the highest affinity for both PtdIns(3,4,5)P3 and PtdIns(3,4)P2. Of the phosphoinositide phosphatases PTEN displayed the highest affinity towards PtdIns(3,4,5)P3 and PtdIns(3,4)P2. The SHIP1 mutant E452K detected in carcinoma patients had a 100-fold increased affinity to PtdIns(3,4)P2 but not to PtdIns(3,4,5)P3 compared to SHIP1 WT. Distinct mutations in phosphoinositide binding proteins like the patient-derived SHIP1E452K mutant may be involved in the upregulation of PI(3,4)P2 -mediated signalling in tumor cells due to phosphoinositide trapping. Our results add further information to the complex hierarchy of phosphoinositide binding proteins helping to elucidate their functional role in cellular signal transduction.

7.
F1000Res ; 102021.
Artigo em Inglês | MEDLINE | ID: mdl-34164109

RESUMO

The current hype associated with machine learning and artificial intelligence often confuses scientists and students and may lead to uncritical or inappropriate applications of computational approaches. Even the field of computer-aided drug design (CADD) is not an exception. The situation is ambivalent. On one hand, more scientists are becoming aware of the benefits of learning from available data and are beginning to derive predictive models before designing experiments. However, on the other hand, easy accessibility of in silico tools comes at the risk of using them as "black boxes" without sufficient expert knowledge, leading to widespread misconceptions and problems. For example, results of computations may be taken at face value as "nothing but the truth" and data visualization may be used only to generate "pretty and colorful pictures". Computational experts might come to the rescue and help to re-direct such efforts, for example, by guiding interested novices to conduct meaningful data analysis, make scientifically sound predictions, and communicate the findings in a rigorous manner. However, this is not always ensured. This contribution aims to encourage investigators entering the CADD arena to obtain adequate computational training, communicate or collaborate with experts, and become aware of the fundamentals of computational methods and their given limitations, beyond the hype. By its very nature, this Opinion is partly subjective and we do not attempt to provide a comprehensive guide to the best practices of CADD; instead, we wish to stimulate an open discussion within the scientific community and advocate rational rather than fashion-driven use of computational methods. We take advantage of the open peer-review culture of F1000Research such that reviewers and interested readers may engage in this discussion and obtain credits for their candid personal views and comments. We hope that this open discussion forum will contribute to shaping the future practice of CADD.


Assuntos
Inteligência Artificial , Desenho de Fármacos , Humanos
8.
J Chem Inf Model ; 61(7): 3255-3272, 2021 07 26.
Artigo em Inglês | MEDLINE | ID: mdl-34153183

RESUMO

Computational methods such as machine learning approaches have a strong track record of success in predicting the outcomes of in vitro assays. In contrast, their ability to predict in vivo endpoints is more limited due to the high number of parameters and processes that may influence the outcome. Recent studies have shown that the combination of chemical and biological data can yield better models for in vivo endpoints. The ChemBioSim approach presented in this work aims to enhance the performance of conformal prediction models for in vivo endpoints by combining chemical information with (predicted) bioactivity assay outcomes. Three in vivo toxicological endpoints, capturing genotoxic (MNT), hepatic (DILI), and cardiological (DICC) issues, were selected for this study due to their high relevance for the registration and authorization of new compounds. Since the sparsity of available biological assay data is challenging for predictive modeling, predicted bioactivity descriptors were introduced instead. Thus, a machine learning model for each of the 373 collected biological assays was trained and applied on the compounds of the in vivo toxicity data sets. Besides the chemical descriptors (molecular fingerprints and physicochemical properties), these predicted bioactivities served as descriptors for the models of the three in vivo endpoints. For this study, a workflow based on a conformal prediction framework (a method for confidence estimation) built on random forest models was developed. Furthermore, the most relevant chemical and bioactivity descriptors for each in vivo endpoint were preselected with lasso models. The incorporation of bioactivity descriptors increased the mean F1 scores of the MNT model from 0.61 to 0.70 and for the DICC model from 0.72 to 0.82 while the mean efficiencies increased by roughly 0.10 for both endpoints. In contrast, for the DILI endpoint, no significant improvement in model performance was observed. Besides pure performance improvements, an analysis of the most important bioactivity features allowed detection of novel and less intuitive relationships between the predicted biological assay outcomes used as descriptors and the in vivo endpoints. This study presents how the prediction of in vivo toxicity endpoints can be improved by the incorporation of biological information-which is not necessarily captured by chemical descriptors-in an automated workflow without the need for adding experimental workload for the generation of bioactivity descriptors as predicted outcomes of bioactivity assays were utilized. All bioactivity CP models for deriving the predicted bioactivities, as well as the in vivo toxicity CP models, can be freely downloaded from https://doi.org/10.5281/zenodo.4761225.


Assuntos
Fígado , Aprendizado de Máquina , Bioensaio , Conformação Molecular
9.
Sci Rep ; 11(1): 8766, 2021 04 22.
Artigo em Inglês | MEDLINE | ID: mdl-33888787

RESUMO

Familial encephalopathy with neuroserpin inclusion bodies (FENIB) is a progressive neurodegenerative disease caused by point mutations in the gene for neuroserpin, a serine protease inhibitor of the nervous system. Different mutations are known that are responsible for mutant neuroserpin polymerization and accumulation as inclusion bodies in many cortical and subcortical neurons, thereby leading to cell death, dementia and epilepsy. Many efforts have been undertaken to elucidate the molecular pathways responsible for neuronal death. Most investigations have concentrated on analysis of intracellular mechanisms such as endoplasmic reticulum (ER) stress, ER-associated protein degradation (ERAD) and oxidative stress. We have generated a HEK-293 cell model of FENIB by overexpressing G392E-mutant neuroserpin and in this study we examine trafficking and toxicity of this polymerogenic variant. We observed that a small fraction of mutant neuroserpin is secreted via the ER-to-Golgi pathway, and that this release can be pharmacologically regulated. Overexpression of the mutant form of neuroserpin did not stimulate cell death in the HEK-293 cell model. Finally, when treating primary hippocampal neurons with G392E neuroserpin polymers, we did not detect cytotoxicity or synaptotoxicity. Altogether, we report here that a polymerogenic mutant form of neuroserpin is secreted from cells but is not toxic in the extracellular milieu.

10.
Bioorg Chem ; 107: 104603, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33429229

RESUMO

LpxC inhibitors represent a promising class of novel antibiotics selectively combating Gram-negative bacteria. In chiral pool syntheses starting from D- and L-xylose, a series of four 2r,3c,4t-configured C-furanosidic LpxC inhibitors was obtained. The synthesized hydroxamic acids were tested for antibacterial and LpxC inhibitory activity, the acquired biological data were compared with those of previously synthesized C-furanosides, and molecular docking studies were performed to rationalize the observed structure-activity relationships. Additionally, bacterial uptake and susceptibility to efflux pump systems were investigated for the most promising stereoisomers.


Assuntos
Amidoidrolases/antagonistas & inibidores , Antibacterianos/farmacologia , Inibidores Enzimáticos/farmacologia , Simulação de Acoplamento Molecular , Xilose/farmacologia , Amidoidrolases/metabolismo , Antibacterianos/síntese química , Antibacterianos/química , Relação Dose-Resposta a Droga , Inibidores Enzimáticos/síntese química , Inibidores Enzimáticos/química , Estrutura Molecular , Relação Estrutura-Atividade , Xilose/síntese química , Xilose/química
11.
Chem Res Toxicol ; 34(2): 286-299, 2021 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-32786543

RESUMO

Predicting the structures of metabolites formed in humans can provide advantageous insights for the development of drugs and other compounds. Here we present GLORYx, which integrates machine learning-based site of metabolism (SoM) prediction with reaction rule sets to predict and rank the structures of metabolites that could potentially be formed by phase 1 and/or phase 2 metabolism. GLORYx extends the approach from our previously developed tool GLORY, which predicted metabolite structures for cytochrome P450-mediated metabolism only. A robust approach to ranking the predicted metabolites is attained by using the SoM probabilities predicted by the FAME 3 machine learning models to score the predicted metabolites. On a manually curated test data set containing both phase 1 and phase 2 metabolites, GLORYx achieves a recall of 77% and an area under the receiver operating characteristic curve (AUC) of 0.79. Separate analysis of performance on a large amount of freely available phase 1 and phase 2 metabolite data indicates that achieving a meaningful ranking of predicted metabolites is more difficult for phase 2 than for phase 1 metabolites. GLORYx is freely available as a web server at https://nerdd.zbh.uni-hamburg.de/ and is also provided as a software package upon request. The data sets as well as all the reaction rules from this work are also made freely available.

12.
Chem Res Toxicol ; 34(2): 396-411, 2021 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-33185102

RESUMO

Disturbance of the thyroid hormone homeostasis has been associated with adverse health effects such as goiters and impaired mental development in humans and thyroid tumors in rats. In vitro and in silico methods for predicting the effects of small molecules on thyroid hormone homeostasis are currently being explored as alternatives to animal experiments, but are still in an early stage of development. The aim of this work was the development of a battery of in silico models for a set of targets involved in molecular initiating events of thyroid hormone homeostasis: deiodinases 1, 2, and 3, thyroid peroxidase (TPO), thyroid hormone receptor (TR), sodium/iodide symporter, thyrotropin-releasing hormone receptor, and thyroid-stimulating hormone receptor. The training data sets were compiled from the ToxCast database and related scientific literature. Classical statistical approaches as well as several machine learning methods (including random forest, support vector machine, and neural networks) were explored in combination with three data balancing techniques. The models were trained on molecular descriptors and fingerprints and evaluated on holdout data. Furthermore, multi-task neural networks combining several end points were investigated as a possible way to improve the performance of models for which the experimental data available for model training are limited. Classifiers for TPO and TR performed particularly well, with F1 scores of 0.83 and 0.81 on the holdout data set, respectively. Models for the other studied targets yielded F1 scores of up to 0.77. An in-depth analysis of the reliability of predictions was performed for the most relevant models. All data sets used in this work for model development and validation are available in the Supporting Information.

13.
Mol Inform ; 40(3): e2000105, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33067876

RESUMO

Histone deacetylase 3 (HDAC3) is a potential drug target for treatment of human diseases such as cancer, chronic inflammation, neurodegenerative diseases and diabetes. Machine learning (ML) as an essential cheminformatics approach has been widely used for QSAR modeling. However, none of them has been applied to HDAC3. To this end, we carefully compiled a set of 1098 compounds from the ChEMBL database that have been assayed against HDAC3 and calculated three different sets of molecular features for each compound, i. e. two-dimensional Mordred descriptors, MACCS keys (166 bits) and Morgan2 fingerprints (1024 bits). Five ML classifiers, i. e. k-Nearest Neighbour (KNN), Support Vector Machine (SVM), Random forest (RF), eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) were trained on each feature set and optimized for classification. A total of 15 models were generated and carefully compared, among which the best-performing one was the XGBoost model based on the Morgan2 fingerprints, i. e. XGBoost_morgan2. Evaluated on a well-curated benchmarking set named MUBD-HDAC3, this model achieved a high early ROC enrichment (ROCE0.5 %: 41.02). A further retrospective screening of an annotated chemical library in PubChem demonstrated that the best model could identify 8 novel-scaffold HDAC3 inhibitors while assaying only 1 % of the compounds. To make this model accessible for the scientific community, we developed a python GUI application named HDAC3i-Finder to facilitate prospective screening for HDAC3 inhibitors. The source code of HDAC3i-Finder is available at https://github.com/jwxia2014/HDAC3i-Finder.

14.
Chem Res Toxicol ; 34(2): 330-344, 2021 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-33295759

RESUMO

Skin sensitization potential or potency is an important end point in the safety assessment of new chemicals and new chemical mixtures. Formerly, animal experiments such as the local lymph node assay (LLNA) were the main form of assessment. Today, however, the focus lies on the development of nonanimal testing approaches (i.e., in vitro and in chemico assays) and computational models. In this work, we investigate, based on publicly available LLNA data, the ability of aggregated, Mondrian conformal prediction classifiers to differentiate between non- sensitizing and sensitizing compounds as well as between two levels of skin sensitization potential (weak to moderate sensitizers, and strong to extreme sensitizers). The advantage of the conformal prediction framework over other modeling approaches is that it assigns compounds to activity classes only if a defined minimum level of confidence is reached for the individual predictions. This eliminates the need for applicability domain criteria that often are arbitrary in their nature and less flexible. Our new binary classifier, named Skin Doctor CP, differentiates nonsensitizers from sensitizers with a higher reliability-to-efficiency ratio than the corresponding nonconformal prediction workflow that we presented earlier. When tested on a set of 257 compounds at the significance levels of 0.10 and 0.30, the model reached an efficiency of 0.49 and 0.92, and an accuracy of 0.83 and 0.75, respectively. In addition, we developed a ternary classification workflow to differentiate nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers. Although this model achieved satisfactory overall performance (accuracies of 0.90 and 0.73, and efficiencies of 0.42 and 0.90, at significance levels 0.10 and 0.30, respectively), it did not obtain satisfying class-wise results (at a significance level of 0.30, the validities obtained for nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers were 0.70, 0.58, and 0.63, respectively). We argue that the model is, in consequence, unable to reliably identify strong to extreme sensitizers and suggest that other ternary models derived from the currently accessible LLNA data might suffer from the same problem. Skin Doctor CP is available via a public web service at https://nerdd.zbh.uni-hamburg.de/skinDoctorII/.

15.
J Med Chem ; 63(24): 15243-15257, 2020 12 24.
Artigo em Inglês | MEDLINE | ID: mdl-33152241

RESUMO

Antimicrobial resistance (AMR) is a growing threat with severe health and economic consequences. The available antibiotics are losing efficacy, and the hunt for alternative strategies is a priority. Quorum sensing (QS) controls biofilm and virulence factors production. Thus, the quenching of QS to prevent pathogenicity and to increase bacterial susceptibility to antibiotics is an appealing therapeutic strategy. The phosphorylation of autoinducer-2 (a mediator in QS) by LsrK is a crucial step in triggering the QS cascade. Thus, LsrK represents a valuable target in fighting AMR. Few LsrK inhibitors have been reported so far, allowing ample room for further exploration. This perspective aims to provide a comprehensive analysis of the current knowledge about the structural and biological properties of LsrK and the state-of-the-art technology for LsrK inhibitor design. We elaborate on the challenges in developing novel LsrK inhibitors and point out promising avenues for further research.


Assuntos
Antibacterianos/farmacologia , Proteínas de Bactérias/antagonistas & inibidores , Farmacorresistência Bacteriana/efeitos dos fármacos , Fosfotransferases (Aceptor do Grupo Álcool)/antagonistas & inibidores , Bibliotecas de Moléculas Pequenas/química , Antibacterianos/química , Proteínas de Bactérias/metabolismo , Biofilmes/efeitos dos fármacos , Bactérias Gram-Negativas/metabolismo , Bactérias Gram-Negativas/fisiologia , Bactérias Gram-Positivas/metabolismo , Bactérias Gram-Positivas/fisiologia , Simulação de Acoplamento Molecular , Fosfotransferases (Aceptor do Grupo Álcool)/metabolismo , Percepção de Quorum/efeitos dos fármacos , Bibliotecas de Moléculas Pequenas/farmacologia
16.
Mol Inform ; 39(11): e2000206, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32893453
17.
Mol Inform ; 39(12): e2000171, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-32725781

RESUMO

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

18.
Int J Mol Sci ; 21(10)2020 May 19.
Artigo em Inglês | MEDLINE | ID: mdl-32438666

RESUMO

Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates are commonly reported as averages over all compounds of a test set and do not consider the structural relationship between the individual test compounds and the training instances. In order to obtain a better understanding of the value of ligand-based methods for target prediction, we benchmarked a similarity-based method and a random forest based machine learning approach (both employing 2D molecular fingerprints) under three testing scenarios: a standard testing scenario with external data, a standard time-split scenario, and a scenario that is designed to most closely resemble real-world conditions. In addition, we deconvoluted the results based on the distances of the individual test molecules from the training data. We found that, surprisingly, the similarity-based approach generally outperformed the machine learning approach in all testing scenarios, even in cases where queries were structurally clearly distinct from the instances in the training (or reference) data, and despite a much higher coverage of the known target space.


Assuntos
Descoberta de Drogas , Aprendizado de Máquina , Terapia de Alvo Molecular , Bases de Conhecimento , Reprodutibilidade dos Testes
19.
J Chem Inf Model ; 60(6): 2858-2875, 2020 06 22.
Artigo em Inglês | MEDLINE | ID: mdl-32368908

RESUMO

A plethora of similarity-based, network-based, machine learning, docking and hybrid approaches for predicting the macromolecular targets of small molecules are available today and recognized as valuable tools for providing guidance in early drug discovery. With the increasing maturity of target prediction methods, researchers have started to explore ways to expand their scope to more challenging molecules such as structurally complex natural products and macrocyclic small molecules. In this work, we systematically explore the capacity of an alignment-based approach to identify the targets of structurally complex small molecules (including large and flexible natural products and macrocyclic compounds) based on the similarity of their 3D molecular shape to noncomplex molecules (i.e., more conventional, "drug-like", synthetic compounds). For this analysis, query sets of 10 representative, structurally complex molecules were compiled for each of the 28 pharmaceutically relevant proteins. Subsequently, ROCS, a leading shape-based screening engine, was utilized to generate rank-ordered lists of the potential targets of the 28 × 10 queries according to the similarity of their 3D molecular shapes with those of compounds from a knowledge base of 272 640 noncomplex small molecules active on a total of 3642 different proteins. Four of the scores implemented in ROCS were explored for target ranking, with the TanimotoCombo score consistently outperforming all others. The score successfully recovered the targets of 30% and 41% of the 280 queries among the top-5 and top-20 positions, respectively. For 24 out of the 28 investigated targets (86%), the method correctly assigned the first rank (out of 3642) to the target of interest for at least one of the 10 queries. The shape-based target prediction approach showed remarkable robustness, with good success rates obtained even for compounds that are clearly distinct from any of the ligands present in the knowledge base. However, complex natural products and macrocyclic compounds proved to be challenging even with this approach, although cases of complete failure were recorded only for a small number of targets.


Assuntos
Produtos Biológicos , Descoberta de Drogas , Ligantes , Aprendizado de Máquina , Proteínas
20.
Bioinformatics ; 36(4): 1291-1292, 2020 02 15.
Artigo em Inglês | MEDLINE | ID: mdl-32077475

RESUMO

SUMMARY: The New E-Resource for Drug Discovery (NERDD) is a quickly expanding web portal focused on the provision of peer-reviewed in silico tools for drug discovery. NERDD currently hosts tools for predicting the sites of metabolism (FAME) and metabolites (GLORY) of small organic molecules, for flagging compounds that are likely to interfere with biological assays (Hit Dexter), and for identifying natural products and natural product derivatives in large compound collections (NP-Scout). Several additional models and components are currently in development. AVAILABILITY AND IMPLEMENTATION: The NERDD web server is available at https://nerdd.zbh.uni-hamburg.de. Most tools are also available as software packages for local installation.


Assuntos
Produtos Biológicos , Descoberta de Drogas , Simulação por Computador , Computadores , Internet , Software
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