A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4–78.0%, 4.7–73.8%, and 214–10,543, respectively, compared to those of 62–95%, 0.65–35%, and 20–1200 by structure-based VS and 55–81%, 0.2–0.7%, and 110–795 by other ligand-based VS tools in screening libraries of ≥1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3–87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.     

Identification of estrogen receptor α ligands with virtual screening techniques

Utilization of computer-aided molecular discovery methods in virtual screening (VS) is a cost-effective approach to identify novel bioactive small molecules. Unfortunately, no universal VS strategy can guarantee high hit rates for all biological targets, but each target requires distinct, fine-tuned solutions. Here, we have studied in retrospective manner the effectiveness and usefulness of common pharmacophore hypothesis, molecular docking and negative image-based screening as potential VS tools for a widely applied drug discovery target, estrogen receptor α (ERα). The comparison of the methods helps to demonstrate the differences in their ability to identify active molecules. For example, structure-based methods identified an already known active ligand from the widely-used bechmarking decoy molecule set. Although prospective VS against one commercially available database with around 100,000 drug-like molecules did not retrieve many testworthy hits, one novel hit molecule with pIC₅₀ value of 6.6, was identified. Furthermore, our small in-house compound collection of easy-to-synthesize molecules was virtually screened against ERα, yielding to five hit candidates, which were found to be active in vitro having pIC₅₀ values from 5.5 to 6.5.     

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.     

Drug/nondrug classification using Support Vector Machines with various feature selection strategies

In conjunction with the advance in computer technology, virtual screening of small molecules has been started to use in drug discovery. Since there are thousands of compounds in early-phase of drug discovery, a fast classification method, which can distinguish between active and inactive molecules, can be used for screening large compound collections. In this study, we used Support Vector Machines (SVM) for this type of classification task. SVM is a powerful classification tool that is becoming increasingly popular in various machine-learning applications. The data sets consist of 631 compounds for training set and 216 compounds for a separate test set. In data pre-processing step, the Pearson's correlation coefficient used as a filter to eliminate redundant features. After application of the correlation filter, a single SVM has been applied to this reduced data set. Moreover, we have investigated the performance of SVM with different feature selection strategies, including SVM–Recursive Feature Elimination, Wrapper Method and Subset Selection. All feature selection methods generally represent better performance than a single SVM while Subset Selection outperforms other feature selection methods. We have tested SVM as a classification tool in a real-life drug discovery problem and our results revealed that it could be a useful method for classification task in early-phase of drug discovery.     

GigaMatrix: an ultra high-throughput tool for accessing biodiversity

Recombinant approaches for tapping into the biodiversity present in nature for the discovery of novel enzymes and biosynthetic pathways can result in large gene libraries. Likewise, laboratory evolution techniques can result in large but potentially valuable libraries. Thorough screening of these libraries requires ultra high-throughput methods. The GigaMatrix^™ screening platform addresses this opportunity using reusable high-density plates with 100,000 to 1,000,000 through-hole wells in a microplate footprint. In addition to throughputs of over 10⁷ wells per day, the platform offers a significant reduction in reagent use and waste, has fully integrated automated “cherry picking,” and uses no complicated dispensing equipment. Wells containing putative hits from targeted fluorescent liquid phase assays are revealed by a fluorescent imaging system. Vision-guided robotics are utilized to recover hits by accessing individual 200 μm and smaller wells with a disposable sterile needle. The GigaMatrix platform has proven to be an effective and efficient tool for screening gene libraries for both discovery and evolution applications.     

Protein tyrosine phosphatases: Ligand interaction analysis and optimisation of virtual screening

Docking-based virtual screening is an established component of structure-based drug discovery. Nevertheless, scoring and ranking of computationally docked ligand libraries still suffer from many false positives. Identifying optimal docking parameters for a target protein prior to virtual screening can improve experimental hit rates. Here, we examine protocols for virtual screening against the important but challenging class of drug target, protein tyrosine phosphatases. In this study, common interaction features were identified from analysis of protein–ligand binding geometries of more than 50 complexed phosphatase crystal structures. It was found that two interactions were consistently formed across all phosphatase inhibitors: (1) a polar contact with the conserved arginine residue, and (2) at least one interaction with the P-loop backbone amide. In order to investigate the significance of these features on phosphatase-ligand binding, a series of seeded virtual screening experiments were conducted on three phosphatase enzymes, PTP1B, Cdc25b and IF2. It was observed that when the conserved arginine and P-loop amide interactions were used as pharmacophoric constraints during docking, enrichment of the virtual screen significantly increased in the three studied phosphatases, by up to a factor of two in some cases. Additionally, the use of such pharmacophoric constraints considerably improved the ability of docking to predict the inhibitor's bound pose, decreasing RMSD to the crystallographic geometry by 43% on average. Constrained docking improved enrichment of screens against both open and closed conformations of PTP1B. Incorporation of an ordered water molecule in PTP1B screening was also found to generally improve enrichment. The knowledge-based computational strategies explored here can potentially inform structure-based design of new phosphatase inhibitors using docking-based virtual screening.     

Discovery of high affinity ligands for β2-adrenergic receptor through pharmacophore-based high-throughput virtual screening and docking

Novel high affinity compounds for human β₂-adrenergic receptor (β₂-AR) were searched among the clean drug-like subset of ZINC database consisting of 9,928,465 molecules that satisfy the Lipinski's rule of five. The screening protocol consisted of a high-throughput pharmacophore screening followed by an extensive amount of docking and rescoring. The pharmacophore model was composed of key features shared by all five inactive states of β₂-AR in complex with inverse agonists and antagonists. To test the discriminatory power of the pharmacophore model, a small-scale screening was initially performed on a database consisting of 117 compounds of which 53 antagonists were taken as active inhibitors and 64 agonists as inactive inhibitors. Accordingly, 7.3% of the ZINC database subset (729,413 compounds) satisfied the pharmacophore requirements, along with 44 antagonists and 17 agonists. Afterwards, all these hit compounds were docked to the inactive apo form of the receptor using various docking and scoring protocols. Following each docking experiment, the best pose was further evaluated based on the existence of key residues for antagonist binding in its vicinity. After final evaluations based on the human intestinal absorption (HIA) and the blood brain barrier (BBB) penetration properties, 62 hit compounds have been clustered based on their structural similarity and as a result four scaffolds were revealed. Two of these scaffolds were also observed in three high affinity compounds with experimentally known K_i values. Moreover, novel chemical compounds with distinct structures have been determined as potential β₂-AR drug candidates.     

基于结构的虚拟筛选技术能更广泛应用于现代药物发现?

总结了计算机辅助药物设计目前的状况,重点讨论了基于结构类的虚拟筛选方法,特别是分子对接方法。在药物发现过程中,这些方法除了在早期的从数据库到命中阶段可节省费用外,还能提供其他有用信息吗?本综述试图通过探究大量现有对接方法的优缺点来回答这一问题。结果表明:基于结构的药物设计还没有实现其早期的所有目标,还需要广泛深入地进行研究,应用时也需谨慎。令人兴奋的是当与其他互补的药物设计途径,如基于配体的方法,相结合时基于结构的方法是最好的。从这一点上看,基于结构的药物设计方法在现代药物发现这一多学科的交叉领域中还应该发挥更多的作用。     

SOS–A sample ordering system for delivering “assay-ready” compound plates for drug screening

Many bottlenecks in drug discovery have been addressed with the advent of new assay and instrument technologies. However, storing and processing chemical compounds for screening remains a challenge for many drug discovery laboratories. Although automated storage and retrieval systems are commercially available for medium to large collections of chemical samples, these samples are usually stored at a central site and are not readily accessible to satellite research labs.Drug discovery relies on the rapid testing of new chemical compounds in relevant biological assays. Therefore, newly synthesized compounds must be readily available in various formats to biologists performing screening assays. Until recently, our compounds were distributed in screw cap vials to assayists who would then manually transfer and dilute each sample in an “assay-ready” compound plate for screening. The vials would then be managed by the individuals in an ad hoc manner.To relieve the assayist from searching for compounds and preparing their own assay-ready compound plates, a newly customized compound storage system with an ordering software application was implemented at our research facility that eliminates these bottlenecks. The system stores and retrieves compounds in 1 mL-mini-tubes or microtiter plates, facilitates compound searching by identifier or structure, orders compounds at varying concentrations in specified wells on 96- or 384-well plates, requests the addition of controls (vehicle or reference compounds), etc. The orders are automatically processed and delivered to the assayist the following day for screening. An overview of our system will demonstrate that we minimize compound waste and ensure compound integrity and availability.     

Methods for compound selection focused on hits and application in drug discovery

In the context of virtual screening calculations, a multiple fingerprint-based metric is applied to generate focused compound libraries by database searching. Different fingerprints are used to facilitate a similarity step for database mining, followed by a diversity step to assemble the final library. The method is applied, for example, to build libraries of limited size for hit-to-lead development efforts. In studies designed to inhibit a therapeutically relevant protein–protein interaction, small molecular hits were initially obtained by combined fingerprint- and structure-based virtual screening and used for the design of focused libraries. We review the applied virtual screening approach and report the statistics and results of screening as well as focused library design. While the structures of lead compounds cannot be disclosed, the analysis is thought to provide an example of the interplay of different methods applied in practical lead identification.     

PKRank: a novel learning-to-rank method for ligand-based virtual screening using pairwise kernel and RankSVM

The development of a new drug takes over 10 years and costs approximately US $2.6 billion. Virtual compound screening (VS) is a part of efforts to reduce this cost. Learning-to-rank is a machine learning technique in information retrieval that was recently introduced to VS. It works well because the application of VS requires the ranking of compounds. Moreover, learning-to-rank can treat multiple heterogeneous experimental data because it is trained using only the order of activity of compounds. In this study, we propose PKRank, a novel learning-to-rank method for ligand-based VS that uses a pairwise kernel and RankSVM. PKRank is a general case of the method proposed by Zhang et al. with the advantage of extensibility in terms of kernel selection. In comparisons of predictive accuracy, PKRank yielded a more accurate model than the previous method.     

基于骨架结构分类法的递进式药物筛选

本文报告一种新颖的基于骨架结构分类法的递进式药物筛选(PS-SCA)技术。此技术在美国国立癌症研究所(NCI)的八组细胞水平上的高通量筛选实验数据上进行了测试,结果表明,基于拓扑结构数据的递进式筛选可以极大地降低药物筛选的代价,缩短筛选时间。模拟实验证明,PS-SCA递进式筛选技术包括三个阶段:(1)骨架多样性采样筛选试验:(2)活性化合物发现试验;(3)可忽略的多余筛选试验。运用PS-SCA递进式筛选技术,可以在只筛选20％的化合物情况下找到最有意义的70-80％的活性化合物。而且,这70％-80％的活性化合物中包含了关键的结构骨架。     

Ensemble docking-based virtual screening yields novel spirocyclic JAK1 inhibitors

Small molecule inhibition of Janus kinases (JAKs) has been demonstrated as a viable strategy for the treatment of various inflammatory conditions and continues to emerge in cancer-related indications. In this study, a large supplier database was screened to identify novel chemistry starting points for JAK1. The docking-based screening was followed up by testing ten hit compounds experimentally, out of which five have displayed single-digit micromolar and submicromolar IC₅₀ values on JAK1. Thus, the study was concluded with the discovery of five novel JAK inhibitors from a tiny screening deck with a remarkable hitrate of 50%. The results have highlighted spirocyclic pyrrolopyrimidines with submicromolar JAK1 IC₅₀ values and a preference for JAK1 over JAK2 as potential starting points in developing a novel class of JAK1 inhibitors.     

Exploration of DPP-IV inhibitors with a novel scaffold by multistep in silico screening

Dipeptidyl peptidase-IV (DPP-IV), an enzyme that degrades incretins–hormones that promote insulin secretion–is a therapeutic target for type 2 diabetes, with a number of its inhibitors having been launched as therapies for diabetes. Since adverse effects of these inhibitors have recently been reported, the development of novel DPP-IV inhibitors with higher efficacy and safety is required. We, therefore, screened for novel DPP-IV inhibitors using the combination of an in silico drug discovery technique and a DPP-IV assay system. We initially selected seven candidate compounds as DPP-IV inhibitors from a database consisting of four million compounds by a multistep in silico screening procedure combining pharmacophore-based screening, docking calculation and the analysis of three-dimensional quantitative structure-activity relationship. We then measured the inhibitory activity of the selected compounds and identified a hit compound. In addition, we discuss the structure–activity relationship between the binding mode model and inhibitory activity of the hit compound.     

组合化学及其在药物开发中的应用

介绍了组合化学的基本概念和相关技术。组合化学起源于固相多肽合成,目前在包括药物开发在内的许多领域都得到了成功的应用。采用组合化学技术可以在短时间内迅速制备大量的化合物。介绍了化合物库的合成方法和分析、筛选方法。组合化学技术与高通量筛选技术相结合,在现代药物开发中已经起到了非常重要的作用。     

Towards more accurate pharmacophore modeling: Multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2

Pharmacophore modeling, including ligand- and structure-based approaches, has become an important tool in drug discovery. However, the ligand-based method often strongly depends on the training set selection, and the structure-based pharmacophore model is usually created based on apo structures or a single protein-ligand complex, which might miss some important information. In this study, multicomplex-based method has been suggested to generate a comprehensive pharmacophore map of cyclin-dependent kinase 2 (CDK2) based on a collection of 124 crystal structures of human CDK2-inhibitor complex. Our multicomplex-based comprehensive pharmacophore map contains almost all the chemical features important for CDK2-inhibitor interactions. A comparison with previously reported ligand-based pharmacophores has revealed that the ligand-based models are just a subset of our comprehensive map. Furthermore, one most-frequent-feature pharmacophore model consisting of the most frequent pharmacophore features was constructed based on the statistical frequency information provided by the comprehensive map. Validations to the most-frequent-feature model show that it can not only successfully discriminate between known CDK2 inhibitors and the molecules of focused inactive dataset, but also is capable of correctly predicting the activities of a wide variety of CDK2 inhibitors in an external active dataset. Obviously, this investigation provides some new ideas about how to develop a multicomplex-based pharmacophore model that can be used in virtual screening to discover novel potential lead compounds.     

基于深度神经网络的药物蛋白虚拟筛选

药物的研发是一种投入成本高、耗费时间长且成功率较低的一种研究,为了在药物开发阶段可以快速获得潜在的化合物,针对性地提出一种基于深度神经网络的药物蛋白虚拟筛选的方法。首先从给定数据集中学习如何提取相关特征,获取配体原子和残基类型进行特征分析,快速识别活性分子和非活性分子,然后使用降维方式和K折验证等方法对药物筛选的模型进行处理,最后通过分析富集因子和AUC值验证诱饵化合物与分子蛋白的互相作用验证模型的可靠程度,实验结果表明所提出的筛选方法具有很好的可行性和有效性,有效地加快了虚拟筛选过程。     

Automated Compound Storage and Retrieval System for Microplates and Tubes

As compound libraries continue their exponential growth, assay systems become increasingly complex, and high throughput screening (HTS) technology picks up speed to become ultra-HTS, the need to store more samples and more discrete compound collections in labware of varied dimension, shape, and function, represents a problem of heightening significance for drug discovery laboratories. Adequate storage space is merely the tip of the iceberg. The main challenge comes in finding suitable space- and cost-efficient storage facilities that provide the flexibility, automation, environmental control, and necessary computerization and compound registration and tracking systems that will ensure the utility, security, and integrity of a laboratory's compound and reagent stores. With sample libraries commonly exceeding one million compounds today and companies screening their inventories against an expanding range of targets, the capability for rapid and reliable compound storage and retrieval, cataloging and monitoring, and fast and accurate cherry-picking becomes essential.