On the Topological Sub-Structural Molecular Design (TOSS-MODE) in QSPR/QSAR and Drug Design Research

Abstract

A recently introduced graph-theoretical approach to the study of structure-property-activity relationships is presented. The theoretical approach and the computational strategy for the use of the TOSS-MODE approach are given with details. Several QSPR and QSAR applications are reviewed including the study of physical properties of organic compounds, diamagnetic susceptibilities, and biological properties. The applications of the TOSS-MODE approach to discrimination of active/inactive compounds, the virtual screening of compounds with a desired property from databases of chemical structures, identification of active/inactive fragments and its relationships with 2D/3D pharmacophores, and to the design of novel compounds with desired biological activities are also reviewed.     

Modeling diamagnetic and magnetooptic properties of organic compounds with the TOSS-MODE approach

The topological substructural molecular design (TOSS-MODE) approach is used to describe the diamagnetic susceptibility of organic compounds. Two data sets composed of 233 aliphatic and 85 aromatic compounds were studied for which good linear correlations were found. The contributions of many different structural fragments and atomic groups were computed by the current approach. The predictive ability of the models developed was tested by using external prediction sets of compounds of different classes than those used in training. A quantitative model based on the current approach was developed to compute the diamagnetic susceptibility exaltation of aromatic compounds, which is exemplified by the study of polycyclic aromatic hydrocarbons. The rotatory power of organic compounds in a magnetic field was also described by the TOSS-MODE approach. Good linear correlations were obtained for this property in aliphatic and aromatic compounds. The predictive abilities of the models found were tested by external prediction sets for which good correlations between calculated and experimental values are found.     

Scaffold hopping in drug discovery using inductive logic programming

In chemoinformatics, searching for compounds which are structurally diverse and share a biological activity is called scaffold hopping. Scaffold hopping is important since it can be used to obtain alternative structures when the compound under development has unexpected side-effects. Pharmaceutical companies use scaffold hopping when they wish to circumvent prior patents for targets of interest. We propose a new method for scaffold hopping using inductive logic programming (ILP). ILP uses the observed spatial relationships between pharmacophore types in pretested active and inactive compounds and learns human-readable rules describing the diverse structures of active compounds. The ILP-based scaffold hopping method is compared to two previous algorithms (chemically advanced template search, CATS, and CATS3D) on 10 data sets with diverse scaffolds. The comparison shows that the ILP-based method is significantly better than random selection while the other two algorithms are not. In addition, the ILP-based method retrieves new active scaffolds which were not found by CATS and CATS3D. The results show that the ILP-based method is at least as good as the other methods in this study. ILP produces human-readable rules, which makes it possible to identify the three-dimensional features that lead to scaffold hopping. A minor variant of a rule learnt by ILP for scaffold hopping was subsequently found to cover an inhibitor identified by an independent study. This provides a successful result in a blind trial of the effectiveness of ILP to generate rules for scaffold hopping. We conclude that ILP provides a valuable new approach for scaffold hopping.     

Drug design of human dihydrofolate reductase (hDHFR) inhibitors with deep learning

Human dihydrofolate reductase (hDHFR) inhibitors have been a popular research object designed as anti-cancer, anti-malarial, and antibacterial drugs for decades. Besides quantitative structure-activity relationship (QSAR), artificial intelligence (AI) has recently been introduced in numerous professional biological researches, such as molecular drug design and biological activity prediction. In this study, we construct a deep-learning workflow for designing novel hDHFR inhibitors. This workflow mainly includes two networks, as described in the following: The first one is the artificial neural network trained by the molecules selected from the ChEMBL database with experimental hDHFR inhibitions as the label to evaluate the bioactivity of the designed molecular structures constructed from the second network. The second network utilizes conditional generative and adversarial networks (cGAN) to generate candidate molecules with the desired properties. Finally, the obtained candidate molecules with high hDHFR inhibition are subjected to a molecular docking process to verify their binding patterns and affinity strengths inside the active site of hDHFR. In the end, we have successfully identified several novel drug-like compounds with hDHFR inhibition comparable to those currently used in clinics. We present a new tool to effectively design new drug-like compounds through an AI approach.     

A machine learning approach to computer-aided molecular design

Summary Preliminary results of a machine learning application concerning computer-aided molecular design applied to drug discovery are presented. The artificial intelligence techniques of machine learning use a sample of active and inactive compounds, which is viewed as a set of positive and negative examples, to allow the induction of a molecular model characterizing the interaction between the compounds and a target molecule. The algorithm is based on a twofold phase. In the first one — the specialization step — the program identifies a number of active/inactive pairs of compounds which appear to be the most useful in order to make the learning process as effective as possible and generates a dictionary of molecular fragments, deemed to be responsible for the activity of the compounds. In the second phase — the generalization step — the fragments thus generated are combined and generalized in order to select the most plausible hypothesis with respect to the sample of compounds. A knowledge base concerning physical and chemical properties is utilized during the inductive process.     

Stationary phases with special structural properties for high-throughput separation techniques: preparation, characterization and applications

Stationary phases with specific structural properties for high-throughput liquid chromatographic (LC) techniques are described. Special attention was paid to phases with special structural properties, mainly containing internal functional group (e.g. amide). Such materials are generally called "embedded phases". There are phases created in amidation process of aminopropylated silica gel, especially phases based on biological compounds, like phospholipids and cholesterol, which are called immobilized artificial membranes (IAM's). The synthesis and applications of polar embedded amide LC stationary phases were also reviewed. Methods of characterization of synthesized packing materials were presented, with general focusing on spectroscopic measurements like (13C and 29Si CP/MAS NMR and FT-IR), elemental and thermal analysis as well as chromatographic quantitative structure-retention relationships (QSRR) and extended chemometric tests. The potential applications of various dedicated stationary phases in a high-throughput LC screening procedures were also presented.     

CAGED CHOLINERGIC LIGANDS AND PHOTOREGULATION OF CHOLINESTERASE ACTIVITIES

Photolabile precursors of biologically interesting molecules, or "caged" compounds can provide control of temporal and spatial release of desired molecules by rapid photolysis, and are thus important tools in the study of fast biological processes. Acetylcholinesterase (AChE) is a particularly fast enzynie which hydrolyses the neurotransmitter acetylcholine with a turnover number approaching 20000 s^-1. Knowledge of the 3-D structure of AChE[1] has permitted a better understanding of structure-function relationships in the cholinesterases, but has also raised cogent new questions concerning the traffic of substrate and products to and from the active site. Time-resolved crystallography would present an ideal approach to investigate this issue at the atomic level and in real time,provided that suitable probes, which regulate the enzymatic activity by temporally and spatially controled release of enzyme substrates or product.     

Prediction of internal standards in reversed-phase liquid chromatography. 1. Initial study on predicting internal standards for use with neutral samples based on linear solvation energy relationships

This paper describes the results of an initial study on the application of linear solvation energy relationships (LSERs) to the prediction of internal standard compounds in reversed-phase liquid chromatographic (RPLC) method development. Six neutral samples are separated on an Inertsil ODS(3) column by either acetonitrile-water or methanol-water mobile phases under either isocratic or linear gradient conditions. After the separation conditions are optimized, the desired positions for internal standard candidates are selected based on the "open windows" of the chromatograms. The compounds with the desired retention range are then predicted based on LSERs from a database consisting of more than 700 compounds with defined physicochemical properties. The prediction requires the use of LSER coefficients under the separation conditions for each sample. They are determined a priori by performing multivariable linear regression on the retention of 20 reference solutes against their physicochemical properties. It can be concluded from the study that LSER is an excellent approach to the selection of internal standard compounds for RPLC under either isocratic or gradient elution. The average prediction error is usually within 10%, but no more than 20%. Finally, LSER approach is fast and systematic, and will save a significant amount of time and resources during RPLC method development.     

SAR analysis of synthetic neolignans and related compounds which are anti-leishmaniasis active compounds using pattern recognition methods

Potentially active new neolignan and analogues against leishmaniasis are proposed. Structure-activity relationship (SAR) techniques were employed. Physicochemical properties such as log P, molecular volume, atomic charge and quantum chemical parameters were calculated for a group of synthetic substances for which the biological activities against leishmaniasis are known. Only about half a dozen out of more than twenty parameters were found to be efficient for the classification of the compounds into active and inactive groups.     

支撑磷脂双层膜的研究进展

支撑磷脂双层膜(supported phospholipid bilayers,SPBs)是细胞膜研究中普及的模型,是固定生物活性物质的理想材料,不仅可以保持生物分子的活性,还能有效抑制其他生物分子的非特异性吸附,在跨膜蛋白、仿生膜、水处理、生物医学和生物传感器等研究领域具有广泛的应用前景。本文介绍了支撑磷脂双层膜的表征方法和制备方法,包括Langmuir Blodgett(LB)膜提拉法、囊泡融合法和LB膜提拉法与囊泡融合联合法;详细阐述了囊泡融合法制备SPBs的机理;综述了囊泡融合法制备SPBs的影响因素,包括囊泡浓度、缓冲溶液、温度、囊泡和基底表面电荷等因素;列举了支撑磷脂膜的应用,并展望了支撑磷脂双层膜的研究趋势。     

Regioselective and Stereospecific Deuteration of Bioactive Aza Compounds by the Use of Ruthenium Nanoparticles

An efficient H/D exchange method allowing the deuteration of pyridines, quinolines, indoles, and alkyl amines with D₂ in the presence of Ru@PVP nanoparticles is described. By a general and simple procedure involving mild reaction conditions and simple filtration to recover the labeled product, the isotopic labeling of 22 compounds proceeded in good yield with high chemo‐ and regioselectivity. The viability of this procedure was demonstrated by the labeling of eight biologically active compounds. Remarkably, enantiomeric purity was conserved in the labeled compounds, even though labeling took place in the vicinity of the stereogenic center. The level of isotopic enrichment observed is suitable for metabolomic studies in most cases. This approach is also perfectly adapted to tritium labeling because it uses a gas as an isotopic source. Besides these applications to molecules of biological interest, this study reveals a rich and underestimated chemistry on the surface of ruthenium nanoparticles.     

Bile Acid Conjugates with Anticancer Activity: Most Recent Research

The advantages of a treatment modality that combines two or more therapeutic agents in cancer therapy encourages the study of hybrid functional compounds for pharmacological applications. In light of this, we reviewed recent works on hybrid molecules based on bile acids. Due to their biological properties, as well as their different chemical/biochemical reactive moieties, bile acids can be considered very interesting starting molecules for conjugation with natural or synthetic bioactive molecules.     

Rational design,properties, and applications of biosurfactants: a short review of recent advances

Biosurfactants combine physicochemical properties with biological activities. Although biosurfactants are often expressed by microorganisms, an increasing amount is produced by chemical synthesis. As many exist in the form of homologous compounds, it is often difficult to purify biosurfactants. But this has not limited the efforts to develop their commercial applications. In this short review, we have featured the recent advances in three important types of biosurfactants, lipopeptides, nucleolipids, and glycolipids. We have focused on comparing some of the key properties and functionalities between modern synthetic versions and their corresponding natural counterparts. We end the review by outlining the needs for not only strengthening their basic structure–property relationships through further research but also developing better technologies, irrespective of direct chemical synthesis or biological synthesis of biosurfactants through constructions of genetically engineered strains, to help advance the commercial use of biosurfactants.