Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.     

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design

The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and beyond. Drug development is a costly and time-consuming business, and only a minority of approved drugs generate returns exceeding the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper reviews the most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research.     

Recent Advances in Application of Computer-Aided Drug Design in Anti-Influenza A Virus Drug Discovery

Influenza A is an acute respiratory infectious disease caused by the influenza A virus, which seriously threatens global human health and causes substantial economic losses every year. With the emergence of new viral strains, anti-influenza drugs remain the most effective treatment for influenza A. Research on traditional, innovative small-molecule drugs faces many challenges, while computer-aided drug design (CADD) offers opportunities for the rapid and effective development of innovative drugs. This literature review describes the general process of CADD, the viral proteins that play an essential role in the life cycle of the influenza A virus and can be used as therapeutic targets for anti-influenza drugs, and examples of drug screening of viral target proteins by applying the CADD approach. Finally, the main limitations of current CADD strategies in anti-influenza drug discovery and the field’s future directions are discussed.     

一种简化的萃取溶剂分子设计方法

本研究提出一种新的萃取溶剂分子设计策略,首先以溶剂选择性为标准预选官能团,缩小分子设计的范围;再应用遗传算法对预选出的官能团进行组合,设计出符合要求的萃取溶剂分子。采用本设计的方法模拟计算了两个萃取溶剂设计实例,获得了令人满意的效果。     

Fluorine in Drug Design: A Case Study with Fluoroanisoles

Anisole and fluoroanisoles display distinct conformational preferences, as evident from a survey of their crystal structures. In addition to altering the free ligand conformation, various degrees of fluorination have a strong impact on physicochemical and pharmacokinetic properties. Analysis of anisole and fluoroanisole matched molecular pairs in the Pfizer corporate database reveals interesting trends: 1) PhOCF₃ increases log D by ～1 log unit over PhOCH₃ compounds; 2) PhOCF₃ shows lower passive permeability despite its higher lipophilicity; and 3) PhOCF₃ does not appreciably improve metabolic stability over PhOCH₃. Emerging from the investigation, difluoroanisole (PhOCF₂H) strikes a better balance of properties with noticeable advantages of log D and transcellular permeability over PhOCF₃. Synthetic assessment illustrates that the routes to access difluoroanisoles are often more straightforward than those for trifluoroanisoles. Whereas replacing PhOCH₃ with PhOCF₃ is a common tactic to optimize ADME properties, our analysis suggests PhOCF₂H may be a more attractive alternative, and greater exploitation of this motif is recommended.     

Systematic Assessment of Fragment Identification for Multitarget Drug Design

Designed multitarget ligands are a popular approach to generating efficient and safe drugs, and fragment-based strategies have been postulated as a versatile avenue to discover multitarget ligand leads. To systematically probe the potential of fragment-based multiple ligand discovery, we have employed a large fragment library for comprehensive screening on five targets chosen from proteins for which multitarget ligands have been successfully developed previously (soluble epoxide hydrolase, leukotriene A4 hydrolase, 5-lipoxygenase, retinoid X receptor, farnesoid X receptor). Differential scanning fluorimetry served as primary screening method before fragments hitting at least two targets were validated in orthogonal assays. Thereby, we obtained valuable fragment leads with dual-target engagement for six out of ten target combinations. Our results demonstrate the applicability of fragment-based approaches to identify starting points for polypharmacological compound development with certain limitations.     

分子构象与药物分子设计

分子构象与分子生物活性有着密切的关系,通过研究分子的构效关系,可以有效避免或缓解繁复的实验研究过程,分子构象识别与分析已成为分子设计的重要研究内容。本文对分子构象在药物分子设计中的作用做了介绍,针对计算机辅助药物设计中受体与配体分子构象的识别过程,进行了分类和阐述,最后就其中存在的问题进行了展望。     

Carboxylic Acid (Bio)Isosteres in Drug Design

The carboxylic acid functional group can be an important constituent of a pharmacophore, however, the presence of this moiety can also be responsible for significant drawbacks, including metabolic instability, toxicity, as well as limited passive diffusion across biological membranes. To avoid some of these shortcomings while retaining the desired attributes of the carboxylic acid moiety, medicinal chemists often investigate the use of carboxylic acid (bio)isosteres. The same type of strategy can also be effective for a variety other purposes, for example, to increase the selectivity of a biologically active compound or to create new intellectual property. Several carboxylic acid isosteres have been reported, however, the outcome of any isosteric replacement cannot be readily predicted as this strategy is generally found to be dependent upon the particular context (i.e., the characteristic properties of the drug and the drug–target). As a result, screening of a panel of isosteres is typically required. In this context, the discovery and development of novel carboxylic acid surrogates that could complement the existing palette of isosteres remains an important area of research. The goal of this Minireview is to provide an overview of the most commonly employed carboxylic acid (bio)isosteres and to present representative examples demonstrating the use and utility of each isostere in drug design.     

多目标进化算法求解过程综合中的混合整数线性规划(MILP)与混合整数非线性规划问题(MINLP)

Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of crame is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.     

Perspectives on Inhibiting β‐Amyloid Aggregation through Structure‐Based Drug Design

Targeting β‐amyloid (Aβ) remains the most desired strategy in Alzheimer’s disease (AD) drug discovery research. Many peptides that specifically target Aβ aggregates are known, encompassing efforts from both industrial and academic research settings. However, in clinical terms, not much success has been gained with peptide research; in turn, small drug‐like molecules are already globally recognized as showing promise as an alternate approach. Aβ aggregation inhibitors are the most important part of the multifunctional drug design regimen for treating AD. Unfortunately, rational drug design approaches with small molecules are still in the initial stages. Herein we highlight, update, and elaborate on the structural anatomy of Aβ and known Aβ aggregation inhibitors in hopes of helping to optimize their use in structure‐based drug design approaches toward inhibitors with greater specificity. Furthermore, we present the first review of efforts to target a previously uncharacterized region of acetylcholinesterase: the N‐terminal 7–20 sub‐region, which was experimentally elucidated to participate in Aβ aggregation and deposition.     

Development of specific "drug-like property" rules for carboxylate-containing oral drug candidates

The carboxylate moiety is an important pharmacophore in the medicinal chemist's arsenal and is sometimes an irreplaceable functionality in drug–target interactions. Thus, practical guidance on its use in the most optimized manner would be a welcome addition to rational drug design. Key physicochemical and ADMET‐PK properties from a dataset of drugs containing a carboxylate (COOH) moiety were assembled and compared with those of a broader, general drug dataset. Our main objective was to identify features specific to COOH‐containing oral drugs that could be converted into simple rules delineating the boundaries within which prospective COOH‐containing chemical series and COOH‐containing drug candidates would be reasonably expected to possess properties suitable for oral administration. These specific “drug‐like” property rules include molecular weight, the number of rotatable bonds, the number of hydrogen bond donors and acceptors, predictions of lipophilic character (calculated log P and log D values), topological polar surface area (TPSA), and the pK_a value of the carboxylate moiety. Similar to the various sets of criteria that have emerged over the past decade and which have significantly reshaped the way medicinal chemists think about preferred drug chemical space, we propose these specific COOH “drug‐like” property rules as a guide for the design of superior COOH‐containing drug candidates and as a tool to better manage the liabilities generally associated with the presence of a COOH moiety.     

微波技术在化学药物合成中的应用

随着我国微波技术应用领域的不断扩大,当前,微波技术在化工行业的应用能够有效解决化工企业生产过程中所面临的各项问题。基于此,研究了微波技术在化学药物合成中的应用,并且分析微波化学、微波效应、微波合成与药物化学之间存在的联系,希望能够为相关技术人员提供理论帮助和指导。     

Advances in De Novo Drug Design: From Conventional to Machine Learning Methods

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development.     

搅拌器设计的一般程序

介绍国外搅拌设备制造商在针对用户要求进行设计时的思路。以最简单的液-液搅拌工况为例,分析和说明一个搅拌器设计的一般程序和必备的计算基础资料,可供设计制造者参考。     

数据挖掘方法的评述

决策离不开知识 ,从数据库中采掘知识 ,是解决从大信息量中获取有用知识的有效途径。但是在实际数据库中 ,数据的复杂性 (如信息量大、噪声等 )对数据挖掘方法提出了比机器学习更高的要求 ,这方面的研究正受到越来越多的关注。本文就当前数据挖掘的几种主要方法 ,即神经网络、决策树、粗集和云模型等方法的研究现状进行了评述 ,指出其存在的问题。从总体上看 ,这些方法都有局限性 ,但它们的有机组合具有互补性 ,多方法融合将成为数据挖掘的发展趋势 ,最后指出数据挖掘方法面临的挑战     

化工装置设计中的通道与空间设计探讨

介绍通道与空间在化工设计中的重要性,体现在安装、操作、维修等方面以人身与财产安全为根本目的。结合化工设计实例进行介绍、分析与探讨,并对通道与空间设计的流程提出了自己的看法：＂结合条件、逐一分析、安全第一、优化组合＂。