Support vector machines for the estimation of aqueous solubility

Support Vector Machines (SVMs) are used to estimate aqueous solubility of organic compounds. A SVM equipped with a Tanimoto similarity kernel estimates solubility with accuracy comparable to results from other reported methods where the same data sets have been studied. Complete cross-validation on a diverse data set resulted in a root-mean-squared error = 0.62 and R(2) = 0.88. The data input to the machine is in the form of molecular fingerprints. No physical parameters are explicitly involved in calculations.     

Synthesis and antimalarial activity of novel dihydro-artemisinin derivatives

The Plasmodium falciparum cysteine protease falcipain-2, one of the most promising targets for antimalarial drug design, plays a key role in parasite survival as a major peptide hydrolase within the hemoglobin degradation pathway. In this work, a series of novel dihydroartemisinin derivatives based on (thio)semicarbazone scaffold were designed and synthesized as potential falcipain-2 inhibitors. The in vitro biological assay indicated that most of the target compounds showed excellent inhibition activity against P. falciparum falcipain-2, with IC(50) values in the 0.29-10.63 μM range. Molecular docking studies were performed to investigate the binding affinities and interaction modes for the inhibitors. The preliminary SARs were summarized and could serve as a foundation for further investigation in the development of antimalarial drugs.     

机器学习方法用于选择性环氧化酶-2抑制剂活性预测模型的建立

与传统的非甾体类消炎药相比,选择性环氧化酶-2抑制剂具有无胃肠道粘膜损伤,溃疡和肾功能障碍等严重的副作用,设计选择性环氧化酶-2抑制剂具有重要意义。本文用支持矢量学习机和神经网络两种机器学习方法建立选择性环氧化酶-2抑制剂的活性预测模型,以期为选择性环氧化酶-2抑制剂药物的合成提供先导化合物。我们将467个环氧化酶-2抑制剂用Kennard-Stone方法分为训练集,验证集和独立测试集,对每一抑制剂分子我们计算了463个包含组成描述符和拓扑描述符的分子描述符来表征其分子结构,并通过F-Score方法选取最重要的分子描述符用于分类模型的建立。结果表明,SVM方法通过变量筛选后具有很好的预测能力,其预测正确率达到93.30%。     

Prediction analysis and quality assessment of microwell array images

Microwell arrays are widely used for the analysis of fluorescent‐labelled biomaterials. For rapid detection and automated analysis of microwell arrays, the computational image analysis is required. Support Vector Machines (SVM) can be used for this task. Here, we present a SVM‐based approach for the analysis of microwell arrays consisting of three distinct steps: labeling, training for feature selection, and classification into three classes. The three classes are filled, partially filled, and unfilled microwells. Next, the partially filled wells are analyzed by SVM and their tendency towards filled or unfilled tested through applying a Gaussian filter. Through this, all microwells can be categorized as either filled or unfilled by our algorithm. Therefore, this SVM‐based computational image analysis allows for an accurate and simple classification of microwell arrays.     

机器学习方法用于二氢叶酸还原酶抑制剂的活性预测

分别采用支持向量学习机、人工神经网络、调节性逻辑回归和K-最临近等机器学习方法对761个二氢叶酸还原酶抑制剂建立了其活性分类预测模型. 采用组成描述符和拓扑描述符表征抑制剂的分子结构及物理化学性质, 使用Kennard-Stone方法进行训练集的设计, 并用Metropolis Monte Carlo模拟退火方法作变量选择. 结果表明, 支持向量学习机优于其它机器学习方法, 所得到的最优模型具有较好的预测结果, 其预测正确率为91.62%. 说明通过合适的训练集设计及变量选择, 支持向量学习机方法可以很好地用于二氢叶酸还原酶抑制剂的活性分类预测.     

Synthesis and Extended Activity of Triazole‐Containing Macrocyclic Protease Inhibitors

Peptide‐derived protease inhibitors are an important class of compounds with the potential to treat a wide range of diseases. Herein, we describe the synthesis of a series of triazole‐containing macrocyclic protease inhibitors pre‐organized into a β‐strand conformation and an evaluation of their activity against a panel of proteases. Acyclic azido–alkyne‐based aldehydes are also evaluated for comparison. The macrocyclic peptidomimetics showed considerable activity towards calpain II, cathepsin L and S, and the 20S proteasome chymotrypsin‐like activity. Some of the first examples of highly potent macrocyclic inhibitors of cathepsin S were identified. These adopt a well‐defined β‐strand geometry as shown by NMR spectroscopy, X‐ray analysis, and molecular docking studies.     

Identification of novel Plasmodium falciparum PI4KB inhibitors as potential anti-malarial drugs: Homology modeling,molecular docking and molecular dynamics simulations

The current study was set to discover selective Plasmodium falciparum phosphatidylinositol-4-OH kinase type III beta (pfPI4KB) inhibitors as potential antimalarial agents using combined structure-based and ligand-based drug discovery approach. A comparative model of pfPI4KB was first constructed and validated using molecular docking techniques. Performance of Autodock4.2 and Vina4 software in predicting the inhibitor-PI4KB binding mode and energy was assessed based on two Test Sets: Test Set I contained five ligands with resolved crystal structures with PI4KB, while Test Set II considered eleven compounds with known IC50 value towards PI4KB. The outperformance of Autodock as compared to Vina was reported, giving a correlation coefficient (R²) value of 0.87 and 0.90 for Test Set I and Test Set II, respectively. Pharmacophore-based screening was then conducted to identify drug-like molecules from ZINC database with physicochemical similarity to two potent pfPI4KB inhibitors –namely cpa and cpb. For each query inhibitor, the best 1000 hits in terms of TanimotoCombo scores were selected and subjected to molecular docking and molecular dynamics (MD) calculations. Binding energy was then estimated using molecular mechanics–generalized Born surface area (MM-GBSA) approach over 50 ns MD simulations of the inhibitor-pfPI4KB complexes. According to the calculated MM-GBSA binding energies, ZINC78988474 and ZINC20564116 were identified as potent pfPI4KB inhibitors with binding energies better than those of cpa and cpb, with ΔG_binding ≥ −34.56 kcal/mol. The inhibitor-pfPI4KB interaction and stability were examined over 50 ns MD simulation; as well the selectivity of the identified inhibitors towards pfPI4KB over PI4KB was reported.     

Repurposing of antitumor drug candidate Quisinostat lead to novel spirocyclic antimalarial agents

Antimalarial chemotherapies endowed with effectiveness against drug-resistant parasites and good safety are urgently required in clinical.Our previous research revealed that clinical phase II antitumor drug Quisinostat was a promising antimalarial prototype by inhibiting the activity of Plasmodium falciparum(P.falciparum) histone deacetylase(PfHDAC).Herein,30 novel spirocyclic linker derivatives were designed and synthesized based on Quisinostat as lead compound,and then their antimalarial activities and cytotoxicity were systematically evaluated.Among them,compounds 8 and 27 could effectively eliminate wild-type and multi-drug resistant P.falciparum parasites,and display weakened cytotoxicity and good metabolic stability.Western blot assay demonstrated that they could inhibit PfHDAC activity like Quisinostat.In addition,both 8 and 27 showed certain antimalarial efficacy in rodent malaria model,and the animal toxicity of 8 was significantly improved compared with Quisinostat.Overall,8 and 27 were structurally novel PfHDAC inhibitors and provided prospective prototype for further antimalarial drug research.     

Design and synthesis of screening libraries based on the muurolane natural product scaffold

The plant-derived natural product 14-hydroxy-6,12-muuroloadien-15-oic acid (1) was identified as a unique scaffold that could be chemically elaborated to generate novel lead- or drug-like screening libraries. Prior to synthesis a virtual library was generated and prioritised based on drug-like physicochemical parameters such as log P, log D(5.5), hydrogen bond donors/acceptors, and molecular weight. The natural product scaffold (1) was isolated from the endemic Australian plant Eremophila mitchellii and then utilised in the parallel solution-phase generation of two series of analogues. The first library consisted of six semi-synthetic amide derivatives, whilst the second contained six carbamate analogues. These libraries have been evaluated for antimalarial activity using a chloroquine-sensitive Plasmodium falciparum line (3D7) and several compounds displayed low to moderate activity with IC(50) values ranging from 14 to 33 μM.     

Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library

Malaria, in particular that caused by Plasmodium falciparum , is prevalent across the tropics, and its medicinal control is limited by widespread drug resistance. Cysteine proteases of P. falciparum , falcipain-2 (FP-2) and falcipain-3 (FP-3), are major hemoglobinases, validated as potential antimalarial drug targets. Structure-based virtual screening of a focused cysteine protease inhibitor library built with soft rather than hard electrophiles was performed against an X-ray crystal structure of FP-2 using the Glide docking program. An enrichment study was performed to select a suitable scoring function and to retrieve potential candidates against FP-2 from a large chemical database. Biological evaluation of 50 selected compounds identified 21 diverse nonpeptidic inhibitors of FP-2 with a hit rate of 42%. Atomic Fukui indices were used to predict the most electrophilic center and its electrophilicity in the identified hits. Comparison of predicted electrophilicity of electrophiles in identified hits with those in known irreversible inhibitors suggested the soft-nature of electrophiles in the selected target compounds. The present study highlights the importance of focused libraries and enrichment studies in structure-based virtual screening. In addition, few compounds were screened against homologous human cysteine proteases for selectivity analysis. Further evaluation of structure-activity relationships around these nonpeptidic scaffolds could help in the development of selective leads for antimalarial chemotherapy.     

Design of new plasmepsin inhibitors: a virtual high throughput screening approach on the EGEE grid

Though different species of the genus Plasmodium may be responsible for malaria, the variant caused by P. falciparum is often very dangerous and even fatal if untreated. Hemoglobin degradation is one of the key metabolic processes for the survival of the Plasmodium parasite in its host. Plasmepsins, a family of aspartic proteases encoded by the Plasmodium genome, play a prominent role in host hemoglobin cleavage. In this paper we demonstrate the use of virtual screening, in particular molecular docking, employed at a very large scale to identify novel inhibitors for plasmepsins II and IV. A large grid infrastructure, the EGEE grid, was used to address the problem of large computation resources required for docking hundreds of thousands of chemical compounds on different plasmepsin targets of P. falciparum. A large compound library of about 1 million chemical compounds was docked on 5 different targets of plasmepsins using two different docking software, namely FlexX and AutoDock. Several strategies were employed to analyze the results of this virtual screening approach including docking scores, ideal binding modes, and interactions to key residues of the protein. Three different classes of structures with thiourea, diphenylurea, and guanidino scaffolds were identified to be promising hits. While the identification of diphenylurea compounds is in accordance with the literature and thus provides a sort of "positive control", the identification of novel compounds with a guanidino scaffold proves that high throughput docking can be effectively used to identify novel potential inhibitors of P. falciparum plasmepsins. Thus, with the work presented here, we do not only demonstrate the relevance of computational grids in drug discovery but also identify several promising small molecules which have the potential to serve as candidate inhibitors for P. falciparum plasmepsins. With the use of the EGEE grid infrastructure for the virtual screening campaign against the malaria causing parasite P. falciparum we have demonstrated that resource sharing on an eScience infrastructure such as EGEE provides a new model for doing collaborative research to fight diseases of the poor.     

Visualisation and interpretation of Support Vector Regression models

This paper introduces a technique to visualise the information content of the kernel matrix and a way to interpret the ingredients of the Support Vector Regression (SVR) model. Recently, the use of Support Vector Machines (SVM) for solving classification (SVC) and regression (SVR) problems has increased substantially in the field of chemistry and chemometrics. This is mainly due to its high generalisation performance and its ability to model non-linear relationships in a unique and global manner. Modeling of non-linear relationships will be enabled by applying a kernel function. The kernel function transforms the input data, usually non-linearly related to the associated output property, into a high dimensional feature space where the non-linear relationship can be represented in a linear form. Usually, SVMs are applied as a black box technique. Hence, the model cannot be interpreted like, e.g., Partial Least Squares (PLS). For example, the PLS scores and loadings make it possible to visualise and understand the driving force behind the optimal PLS machinery. In this study, we have investigated the possibilities to visualise and interpret the SVM model. Here, we exclusively have focused on Support Vector Regression to demonstrate these visualisation and interpretation techniques. Our observations show that we are now able to turn a SVR black box model into a transparent and interpretable regression modeling technique.     

Consensus model for identification of novel PI3K inhibitors in large chemical library

Phosphoinositide 3-kinases (PI3Ks) inhibitors have treatment potential for cancer, diabetes, cardiovascular disease, chronic inflammation and asthma. A consensus model consisting of three base classifiers (AODE, kNN, and SVM) trained with 1,283 positive compounds (PI3K inhibitors), 16 negative compounds (PI3K non-inhibitors) and 64,078 generated putative negatives was developed for predicting compounds with PI3K inhibitory activity of IC₅₀ ≤ 10 μM. The consensus model has an estimated false positive rate of 0.75%. Nine novel potential inhibitors were identified using the consensus model and several of these contain structural features that are consistent with those found to be important for PI3K inhibitory activities. An advantage of the current model is that it does not require knowledge of 3D structural information of the various PI3K isoforms, which is not readily available for all isoforms.