Clustering of chemical databases by means of the projection of maximum overlapping sets similarity measurements onto multidimensional spaces

In this paper, we propose a new method for clustering of chemical databases based on the representation of measurements of structural similarity onto multidimensional spaces. The proposed method permits the tuning of the clustering process through the selection of the dimension of the projection space, the normal vectors and the sensibility of the projection process. The structural similarity of each element regarding to the database elements is projected onto the defined spaces generating clusters that represent the characteristics and diversity of the database and whose size and characteristics can be easily adjusted.     

Representation of the molecular topology of cyclical structures by means of cycle graphs. 2. Application to clustering of chemical databases

The great size of chemical databases and the high computational cost required in the atom-atom comparison of molecular structures for the calculation of the similarity between two chemical compounds necessitate the proposal of new clustering models with the aim of reducing the time of recovery of a set of molecules from a database that satisfies a range of similarities with regard to a given molecule pattern. In this paper we make use of the information corresponding to the cycles existing in the structure of molecules as an approach for the classification of chemical databases. The clustering method here proposed is based on the representation of the topological structure of molecules stored in chemical databases through its corresponding cycle graph. This method presents a more appropriate behavior for others described in the bibliography in which the information corresponding to the cyclicity of the molecules is also used.     

Virtual screening and library enumeration of new hydroxycinnamates based antioxidant compounds: A complete framework

Designing of molecules for drugs is important topic from many decades. The search of new drugs is very hard, and it is expensive process. Computer assisted framework can provide the fastest way to design and screen drug-like compounds. In present work, a multidimensional approach is introduced for the designing and screening of antioxidant compounds. Antioxidants play a crucial role in ensuring that the body's oxidizing and reducing species are kept in the proper balance, minimizing oxidative stress. Machine learning models are used to predict antioxidant activity. Three hydroxycinnamates are selected as standard antioxidants. Similar compounds are searched from ChEMBL database using chemical structural similarity method. The libraries of new compounds are generated using evolutionary method. New compounds are also designed using automatic decomposition and construction building blocks. The antioxidant activity of all designed and searched compounds is predicted using machine learning models. The chemical space of searched and generated compounds is envisioned using t-distributed stochastic neighbor embedding (t-SNE) method. Best compounds are shortlisted, and their synthetic accessibility is predicted to further facilitate the experimental chemists. The chemical similarity between standard and selected compounds is also studied using fingerprints and heatmap.     

Similarity searching in databases of flexible 3D structures using smoothed bounded distance matrices

This paper describes a method for calculating the similarity between pairs of chemical structures represented by 3D molecular graphs. The method is based on a graph matching procedure that accommodates conformational flexibility by using distance ranges between pairs of atoms, rather than fixing the atom pair distances. These distance ranges are generated using triangle and tetrangle bound smoothing techniques from distance geometry. The effectiveness of the proposed method in retrieving other compounds of like biological activity is evaluated, and the results are compared with those obtained from other, 2D-based methods for similarity searching.     

Quantum probability ranking principle for ligand-based virtual screening

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.     

基于支持向量学习机方法的人体小肠吸收药物活性的预测

为了预测分子在人体小肠中的吸收,本文计算了表征分子的电子、拓扑、几何结构、分子形状等特征的102个分子描述符,用遗传算法变量选择方法使描述符减少到47个。体系共包含了230个化合物分子,69个不能被吸收(mA-),161个可以被吸收(HIA )。对建立的SVM模型,用5重交叉验证和独立测试集进行验证,预测正确率分别达到79．1％和77．1％,结果具有较好的一致性。在模型验证中,通过聚类分析方法组合训练集和测试集,保证了模型的稳定性,提高了建模效率。     

Toward high throughput 3D virtual screening using spherical harmonic surface representations

Searching chemical databases for possible drug leads is often one of the main activities conducted during the early stages of a drug development project. This article shows that spherical harmonic molecular shape representations provide a powerful way to search and cluster small-molecule databases rapidly and accurately. Our clustering results show that chemically meaningful clusters may be obtained using only low order spherical harmonic expansions. Our database search results show that using low order spherical harmonic shape-based correlation techniques could provide a practical and efficient way to search very large 3D molecular databases, hence leading to a useful new approach for high throughput 3D virtual screening. The approach described is currently being extended to allow the rapid search and comparison of arbitrary combinations of molecular surface properties.     

Hierarchical clustering of infrared spectra

The generation of a hierarchical tree of 500 infrared spectra, using the recently proposed fractal or 3-distances-clustering method is described and discussed. The objects of clustering are infrared spectra of polymer compounds which are represented as sets of 80 complex Fourier coefficients, obtained by fast Fourier transformation of digitized absorbance spectra. The generated hierarchical tree, with a maximum height of 20 and an average height of 12 levels, yields a very satisfactory clustering scheme with respect to the structure of the compounds involved. In addition to very good clustering, a 100% retrieval (prediction) ability was obtained. This was achieved by the use of an iterative procedure after the initial tree had been generated. Additionally, the tree was tested with 240 infrared spectra of different compounds which were taken into account during the generation of the tree. The retrieval success of these test runs is discussed with respect to the structural similarity of the compounds to which the “unknown” spectra were linked.