中药有效成分三维结构数据库的开发和研究

介绍了北京大学中药有效成分三维结构数据库软件系统的结构、功能及开发步骤。该数据库系统不仅仅提供6500个中草药有效成分的二维和三维结构以及其它各类相关信息,同时拥有功能强大的数据库查询、维护及分子表达系统。在该系统中,用户可以交互式地实现多种分子特征的查询以及二维子结构的查询。查询得到的分子可以直接在北京大学药物设计系统(PKUDDS)中进行三维结构的显示和分析。该数据库系统和我们科研组开发的北京大学药物设计系统以及中草药信息系统构成了完整的基于中药的药物设计系统。该系统已经用于NS3-NS4A蛋白酶抑制剂以及其它体系的研究并取得了很好的结果。     

Distribution measurement of amphetamine-type stimulants in organs using micropulverized extraction and liquid chromatography/tandem mass spectrometry to complement drug distribution using mass spectrometry imaging

Amphetamine-type stimulants (ATS) such as methamphetamine are widely abused and can cause toxic effects in the body. In this study, a simple and accurate analytical method for distribution measurement of drugs in organs was developed to visualize localization of ATS in organs and to complement drug distribution by mass spectrometry imaging (MSI). The brain, liver and kidney from rats to which ATS had been administered were segmented into blocks of 2×2×2 mm3 at -30°C. Each organ block was micropulverized with a stainless-steel bullet at -80°C. The concentrations of drugs in each block were measured by liquid chromatography/tandem mass spectrometry. The three-dimensional distribution of drugs in a whole organ was expressed using color gradation of drug concentration after reconstruction of all blocks to the original locations. The distribution was also compared with that obtained by MSI. This method enabled measurement of drug distribution in organs with simple and clean procedures and accurate quantification unlike autoradiography and MSI. The methamphetamine concentrations were different between parts in an organ, particularly in the kidney. This method could be applicable to the measurement of the distribution of compounds in various solid samples and could be used as a complementary method for the measurement of the distribution of compounds by MSI.     

Fragmentation pathways of drugs of abuse and their metabolites based on QTOF MS/MS and MS(E) accurate-mass spectra

A study of the fragmentation pathways of several classes of drugs of abuse (cannabinoids, ketamine, amphetamine and amphetamine-type stimulants (ATS), cocaine and opiates) and their related substances has been made. The knowledge of the fragmentation is highly useful for specific fragment selection or for recognition of related compounds when developing MS-based analytical methods for the trace-level determination of these compounds in complex matrices. In this work, accurate-mass spectra of selected compounds were obtained using liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, performing both MS/MS and MS(E) experiments. As regards fragmentation behavior, the mass spectra of both approaches were quite similar and were useful to study the fragmentation of the drugs investigated. Accurate-mass spectra of 37 drugs of abuse and related compounds, including metabolites and deuterated analogues, were studied in this work, and structures of fragment ions were proposed. The accurate-mass data obtained allowed to confirm structures and fragmentation pathways previously proposed based on nominal mass measurements, although new insights and structure proposals were achieved in some particular cases, especially for amphetamine and ATS, 11-nor-9-carboxy-Δ(9)-tetrahydrocannabinol (THC-COOH) and opiates.     

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.     

Virtual Screening and Structure Generation Applied to Drug Design

The methods of computer-aided drug design can be divided into two categories according to whether or not the structures of receptors are known1, corresponding to two principal strategies: (1) searching the bio-active ligands against virtual combinatorial libraries and calculating the affinity energy between ligand and receptor by docking ; (2) QSAR and 3D-structure data-mining. 3D-QSAR method is now applied widely to drug discovery, but this method is generally limited to refine the structu…     

De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks

An evolutionary algorithm was developed for fragment-based de novo design of molecules (TOPAS, TOPology-Assigning System). This stochastic method aims at generating a novel molecular structure mimicking a template structure. A set of 25,000 fragment structures serves as the building block supply, which were obtained by a straightforward fragmentation procedure applied to 36,000 known drugs. Eleven reaction schemes were implemented for both fragmentation and building block assembly. This combination of drug-derived building blocks and a restricted set of reaction schemes proved to be a key for the automatic development of novel, synthetically tractable structures. In a cyclic optimization process, molecular architectures were generated from a parent structure by virtual synthesis, and the best structure of a generation was selected as the parent for the subsequent TOPAS cycle. Similarity measures were used to define `fitness', based on 2D-structural similarity or topological pharmacophore distance between the template molecule and the variants. The concept of varying library `diversity' during a design process was consequently implemented by using adaptive variant distributions. The efficiency of the design algorithm was demonstrated for the de novo construction of potential thrombin inhibitors mimicking peptide and non-peptide template structures.     

Recent applications of capillary electrophoresis-electrospray ionisation-mass spectrometry in drug analysis

This review considers applications in 2004-2005 of capillary electrophoresis-electrospray ionisation-mass spectrometry (CE-ESI-MS) to the detection and determination of small molecular mass drug molecules, taken from the Web of Knowledge database. The molecules of small molecular mass less than 1000 Da are chosen according to selected structural classes in which they give ESI signals primarily as [M + H](+) ions. These structural classes are drugs with amine-containing side chains, drugs with N-containing saturated ring structures, 1,4-benzodiazepines, other heterocyclic hypnotics, steroids, bioactive compounds containing phenolic groups, and miscellaneous molecules. Details are given on the fragmentations, where available, that these ionic species exhibit in-source and in ion-trap, triple quadrupole and time-of flight mass spectrometers. The review then gives a critical evaluation of these recent CE-ESI-MS analytical methods for the detection and determination of these small molecular mass drug molecules. Analytical information on, for example, sample concentration techniques, CE separation conditions, recoveries from biological media and limits of detection are provided.     

Recent studies of the electrospray ionisation behaviour of selected drugs and their application in capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry

This review is concerned with recent studies of electrospray ionisation-mass spectrometry (ESI-MS) of selected small molecular mass drugs and their application in qualitative and quantitative analytical methods using the techniques liquid chromatography mass spectrometry (LC-ESI-MS) and capillary electrophoresis mass spectrometry (CE-ESI-MS). The publications reviewed are taken from the Web of Knowledge database for the year 2006. The drugs have molecular mass less than 1000 Da and are chosen according to selected drug classifications in which they give ESI signals primarily as [M+H]+ ions. The drug classifications are antibiotics/antibacterials, steroids, anti-tumour drugs, erectile dysfunction agents, anti-epileptic drugs, antiasthmatic drugs, psychoactive drugs and miscellaneous drugs. Details are given on the fragmentations, where available, that these ionic species exhibit in-source and in ion trap, triple quadrupole and time-of-flight mass spectrometers. Analytical methods for the detection and determination of these small molecular mass drug molecules are also discussed, where appropriate, under the particular drug classifications. Analytical information on, for example, sample concentration techniques, separation conditions, recoveries from biological media and limits of detection/quantitation (LODs and LOQs) are provided.     

Discovery of Anti-SARS Coronavirus Drug Based on Molecular Docking and Database Screening

The active site of 3CL proteinase (3CL^por) for coronavirus was identified by comparing the crystal structures of human and porcine coronavirus. The inhibitor of the main protein of rhinovirus (Ag7088) could bind with 3CL^pro of human coronavirus, then it was selected as the reference for molecular docking and database screening. The ligands from two databases were used to search potential lead structures with molecular docking. Several structures from natural products and ACD-SC databases were found to have lower binding free energy with 3CL^pro than that of Ag7088. These structures have similar hydrophobicity to Ag7088. They have complementary electrostatic potential and hydrogen bond aeceptor and donor with 3CL^pro, showing that the strategy of anti-SARS drug design based on molecular docking and database screening is feasible.     

Rapid protein-ligand costructures using chemical shift perturbations

Structure-based drug discovery requires the iterative determination of protein-ligand costructures in order to improve the binding affinity and selectivity of potential drug candidates. In general, X-ray and NMR structure determination methods are time consuming and are typically the limiting factor in the drug discovery process. The application of molecular docking simulations to filter and evaluate drug candidates has become a common method to improve the throughput and efficiency of structure-based drug design. Unfortunately, molecular docking methods suffer from common problems that include ambiguous ligand conformers or failure to predict the correct docked structure. A rapid approach to determine accurate protein-ligand costructures is described based on NMR chemical shift perturbation (CSP) data routinely obtained using 2D 1H-15N HSQC spectra in high-throughput ligand affinity screens. The CSP data is used to both guide and filter AutoDock calculations using our AutoDockFilter program. This method is demonstrated for 19 distinct protein-ligand complexes where the docked conformers exhibited an average rmsd of 1.17 +/- 0.74 A relative to the original X-ray structures for the protein-ligand complexes.     

A new system,tutors, for computer-aided molecular design

Computer-aided molecular design by TUTORS (TUTORial System) which is under development, is described. The system is based on two types of process, information reduction and information integration. TUTORS consists of two major subsystems, TUTORS-DB and TUTORS-SG, corresponding to these functions. TUTORS-DB involves management of chemical compound data, molecular modeling and data analysis for structure/activity problems. TUTORS-SG involves generation of candidate structures with the required activity by using the knowledge obtained from TUTORS-DB. The major functions of each subsystem and the approach for practical structure design are discussed.     

Efficient machine learning model for predicting drug-target interactions with case study for Covid-19

BackgroundDiscover possible Drug Target Interactions (DTIs) is a decisive step in the detection of the effects of drugs as well as drug repositioning. There is a strong incentive to develop effective computational methods that can effectively predict potential DTIs, as traditional DTI laboratory experiments are expensive, time-consuming, and labor-intensive. Some technologies have been developed for this purpose, however large numbers of interactions have not yet been detected, the accuracy of their prediction still low, and protein sequences and structured data are rarely used together in the prediction process.MethodsThis paper presents DTIs prediction model that takes advantage of the special capacity of the structured form of proteins and drugs. Our model obtains features from protein amino-acid sequences using physical and chemical properties, and from drugs smiles (Simplified Molecular Input Line Entry System) strings using encoding techniques. Comparing the proposed model with different existing methods under K-fold cross validation, empirical results show that our model based on ensemble learning algorithms for DTI prediction provide more accurate results from both structures and features data.ResultsThe proposed model is applied on two datasets:Benchmark (feature only) datasets and DrugBank (Structure data) datasets. Experimental results obtained by Light-Boost and ExtraTree using structures and feature data results in 98 % accuracy and 0.97 f-score comparing to 94 % and 0.92 achieved by the existing methods. Moreover, our model can successfully predict more yet undiscovered interactions, and hence can be used as a practical tool to drug repositioning.A case study of applying our prediction model on the proteins that are known to be affected by Corona viruses in order to predict the possible interactions among these proteins and existing drugs is performed. Also, our model is applied on Covid-19 related drugs announced on DrugBank. The results show that some drugs like DB00691 and DB05203 are predicted with 100 % accuracy to interact with ACE2 protein. This protein is a self-membrane protein that enables Covid-19 infection. Hence, our model can be used as an effective tool in drug reposition to predict possible drug treatments for Covid-19.     

A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases

The discovery of various protein/receptor targets from genomic research is expanding rapidly. Along with the automation of organic synthesis and biochemical screening, this is bringing a major change in the whole field of drug discovery research. In the traditional drug discovery process, the industry tests compounds in the thousands. With automated synthesis, the number of compounds to be tested could be in the millions. This two-dimensional expansion will lead to a major demand for resources, unless the chemical libraries are made wisely. The objective of this work is to provide both quantitative and qualitative characterization of known drugs which will help to generate "drug-like" libraries. In this work we analyzed the Comprehensive Medicinal Chemistry (CMC) database and seven different subsets belonging to different classes of drug molecules. These include some central nervous system active drugs and cardiovascular, cancer, inflammation, and infection disease states. A quantitative characterization based on computed physicochemical property profiles such as log P, molar refractivity, molecular weight, and number of atoms as well as a qualitative characterization based on the occurrence of functional groups and important substructures are developed here. For the CMC database, the qualifying range (covering more than 80% of the compounds) of the calculated log P is between -0.4 and 5.6, with an average value of 2.52. For molecular weight, the qualifying range is between 160 and 480, with an average value of 357. For molar refractivity, the qualifying range is between 40 and 130, with an average value of 97. For the total number of atoms, the qualifying range is between 20 and 70, with an average value of 48. Benzene is by far the most abundant substructure in this drug database, slightly more abundant than all the heterocyclic rings combined. Nonaromatic heterocyclic rings are twice as abundant as the aromatic heterocycles. Tertiary aliphatic amines, alcoholic OH and carboxamides are the most abundant functional groups in the drug database. The effective range of physicochemical properties presented here can be used in the design of drug-like combinatorial libraries as well as in developing a more efficient corporate medicinal chemistry library.     

智能中空药物载体的门控设计

由于具有独特新颖的结构和广泛的应用领域,中空材料已成为合成化学和材料化学研究的热点;特别是其高的表面体积比、低密度及大空腔等特点,成为药物递送载体的最佳选择.通过对中空结构的精确选择和精准修饰,可赋予中空材料独特的刺激响应行为,从而实现该类药物载体的智能设计和药物的可控释放.目前,构建中空智能载体主有以下两条思路:(1)利用自身可对环境中的物理化学刺激做出响应的中空材料作为载体;(2)在中空载体表面修饰功能性分子,以实现在特定的刺激下精确控制孔道的“开-关”转换.其核心在于分子组成和构型的精准调控.基于此,本文综合评述了中空智能载体的可控释放机制.首先介绍中空药物载体的发展历史,随后阐述药物分子在中空结构中的扩散规律,并总结了中空结构载体的智能响应行为、不同的门控机制、控制释放原理以及应用前景,最后对未来的发展做了展望.