Computational Approaches to Drug Design

The rational approach to pharmaceutical drug design begins with an investigation of the relationship between chemical structure and biological activity. Information gained from this analysis is used to aid the design of new, or improved, drugs. Primary considerations during this investigation are the geometric and chemical characteristics of the molecules. Computational chemists who are involved in rational drug design routinely use an array of programs to compute, among other things, molecular surfaces and molecular volume, models of receptor sites, dockings of ligands inside protein cavities, and geometric invariants among different molecules that exhibit similar activity. There is a pressing need for efficient and accurate solutions to the above problems. {Often, limiting assumptions need to be made, in order to make the calculations tractable. Also,} the amount of data processed when searching for a potential drug is currently very large and is only expected to grow larger in the future. This paper describes some areas of computer-aided drug design that are important to computational chemists but are also rich in algorithmic problems. It surveys recent work in these areas both from the computational chemistry and the computer science literature. Received June 5, 1997; revised June 20, 1998.     

Towards a New Logic for Front End Management: From Drug Discovery to Drug Design in Pharmaceutical R&D

Under pressure to innovate and be cost‐effective at the same time, R&D departments are being challenged to develop new organizations and processes for Front End activities. This is especially true in the pharmaceutical industry. As drug development becomes more risky and costly, the discovery departments of pharmaceutical companies are increasingly being compelled to provide strong drug candidates for efficient development processes and quick market launches. It is argued that the Fuzzy Front End consists less of the discovery or recognition of opportunities than of the building of expanded concepts: the notion of concept generation is revisited, suggesting the need for a new logic for organizing Front End activities in order to support sustainable innovative product development. Based on an in‐depth empirical study at a European pharmaceutical company, this paper contributes to improved understanding of the actual management practices used in the Front End. Using a design reasoning model (the C‐K model), it also adds to the growing body of literature on the management of Front End activities in new product development processes.     

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed. Sentiment analysis of drug reviews has a large potential for providing valuable insights into these cases. Therefore, this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques. A dataset acquired from the ‘Drugs.Com’ containing reviews of drug-related side effects and reactions, is used for experiments. A lexicon-based approach, Textblob is used to extract the positive, negative or neutral sentiment from the review text. Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory (CNN-LSTM) network. The CNN is used at the first level to extract the appropriate features while LSTM is used at the second level. Several well-known machine learning models including logistic regression, random forest, decision tree, and AdaBoost are evaluated using term frequency-inverse document frequency (TF-IDF), a bag of words (BoW), feature union of (TF-IDF + BoW), and lexicon-based methods. Performance analysis with machine learning models, long short term memory and convolutional neural network models, and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy. We also performed a statistical significance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.     

Application of a Recurrent Neural Network to Prediction of Drug Dissolution Profiles

The Elman Recurrent Neural Network was employed for the prediction of in-vitro dissolution profiles of matrix controlled release theophylline pellet preparation, leading to the potential use of an intelligent learning system in the development of pharmaceutical products with desired drug release characteristics. A total of six different formulations containing various matrix ratios of substance to control the release rate of theophylline were used for experimentation. By using the leave-one-out cross-validation approach, the dissolution profiles of all the matrix ratios were consumed for training, except for one set that was taken as a reference profile, with which the network predicted profiles were compared. Performance of the network was assessed using the similarity factor, f ₂ , a criterion for dissolution profile comparison recommended by the United States Food and Drug Administration. Simulation results indicated that the Elman network was capable of predicting dissolution profiles that were similar to the reference profiles with an error of less than 8%. In addition, the Bootstrap method was used to estimate the confidence intervals of the f ₂ values. The results revealed the potential of a neural-network-based intelligent system in solving non-linear time-series prediction problems in pharmaceutical product development.     

High Throughput Drug Profiling

With the development of high throughput screening (HTS) during the last two decades new technologies have gained access to chemistry and biology laboratories. The use of both, laboratory robotics and automated workstations has greatly increased the number of chemical entities that are synthesized for and tested against new targets. While a decade ago a daily throughput of about 1,000 compounds was considered sufficient, nowadays screening laboratories aim to achieve 100 times as many samples in the same period of time. Combinatorial chemistry vice versa has increased comparably the daily output and HTS has become an important success factor during early lead finding. Nearly all drug discovery research projects in pharmaceutical industry employ HTS screening assays as initial steps to discover the chemical leads. These compounds provide the structural basis for further medicinal chemistry activities that focuses on optimization of the lead with respect to the activity and selectivity profile in order to identify the development candidate.     

智能药品配送机器人

基于SLAM导航和计算机视觉技术自主研发了一款药品配送机器人用于代替医护人员按时按需为患者配送药品.机器人以US-C5300为核心主控,采用Linux操作系统,底部搭载12V电机以及全向轮给予机器人驱动力,同时配合激光雷达、摄像头、光敏传感器、扬声器、智能药盒等硬件设备,实现机器人自主寻径导航、人脸识别患者身份、摔倒检...     

Boston Innovation''s SmartPlate Integrates Laboratory Functions to Increase Efficiency and Flexibility of Drug Discovery

Assay development is often facilitated by testing the chemistry against a representative or focused library of molecular compounds. Boston Innovation Inc. (“BII”), has introduced its SmartPlate technology which integrates molecular compound storage and dispensing in a single tool which enables many assay iterations quickly, easily, and at low cost, while maintaining consistent compound integrity. SmartPlate does this by storing concentrated compounds in 100% DMSO in sealed reservoirs, each with a dedicated dispensing tap. The compounds are never exposed to air, moisture, or light. Risks of cross-contamination and the need for cleaning or disposing pipette tips are eliminated. Compounds are metered and integrally diluted within the dispensing taps using BII's Direct Dilution™ technology, which allows SmartPlate to serve the needs of high density assay platforms with non-contact dispensing from 5 to 200nL of compound directly to standard assay plates. The integration of functions cuts waste, cost, and time from the drug discovery process by maintaining compound integrity, preserving valuable compound inventory, and eliminating process steps. At 15nL compound dispensing, 15μL of each compound in SmartPlate can now theoretically support one thousand assays without revisiting the main compound repository.     

Evolutionary Algorithms in Drug Design

Designing a drug is the process of finding or creating a molecule which has a specific activity on a biological organism. Drug design is difficult since there are only few molecules that are both effective against a certain disease and exhibit other necessary physiological properties, such as absorption by the body and safety of use. The main problem of drug design is therefore how to explore the chemical space of many possible molecules to find the few suitable ones. Computational methods are increasingly being used for this purpose, among them evolutionary algorithms. This review will focus on the applications of evolutionary algorithms in drug design, in which evolutionary algorithms are used both to create new molecules and to construct methods for predicting the properties of real or yet unexisting molecules. We will also discuss the progress and problems of application of evolutionary algorithms in this field, as well as possible developments and future perspectives.     

利用水晶报表管理药品标准提高项目中样品及资料征集工作

水晶报表是一个非常好用的组件,在程序中可以用来处理诸多的事件。本文通过一个实例,详细介绍了水晶报表的两种应用模式,拉模式和推模式。相对来说,拉模式应用比较简单,但不够灵活。推模式使用起来比较复杂,但灵活多变。在不同的情况下两种应用模式都能实现特定的功能。     

基于Monte Carlo模型的微型PLGA给药系统建模及仿真

多腔体的微型可降解高分子聚合物PLGA药物缓释系统是一种新型植入式给药微器件,其载体结构是结合药物释放的要求和高分子聚合物生物降解特性进行设计并利用MEMS工艺制备.为了解微型给药系统实际释药的性能,需要对其进行建模和仿真研究.基于体溶蚀的Monte Carlo溶蚀模型,建立了具有多腔体的微型PLGA给药载体的释药模型,并对腔体结构为圆形的微型给药系统进行了释药过程仿真.仿真结果表明本文建立的微系统释药模型可以较为准确的描述微系统的释药过程,仿真模型对进一步开发微型PLGA给药系统有重要的参考价值.     

基于QR码的药品防伪系统

针对药品防伪现状,以Microsoft Visual Studio 2010为开发平台,运用C#编程语言和SQL Server 2008数据库,开发基于QR码防伪技术和加密技术的药品防伪系统。系统包括QR码生成模块、查询模块、验证模块、查询信息反馈模块、数据统计模块等。应用结果表明,消费者可以方便地查询药品的真伪,基于QR码的药品防伪系统方便实用,安全可靠。     

药品说明书的Web管理系统的设计

为了使医务人员能方便快捷地查询药品说明书的信息,基于ASP.Net2.0技术设计实现了医院药品说明书的web管理系统。文章讨论了系统的需求分析、数据库设计及系统设计实现过程中的一些关键技术。实际应用表明,系统有利于提高药品治疗质量和合理用药水平。     

药物发现网格设计与实现

讨论了药物发现网格（DDGrid）设计与实现，药物发现网格聚集和利用因特网上异构的地理位置不同的资源为药物设计提供了巨大计算能力。分析了药物发现网格体系结构和实现关键技术，以及对功能模块的描述，并通过实验对系统进行了测试以及任务调度分析。     

中药数据库的设计与建立

对中药信息进行了分析,设计了中药数据库的模式,并介绍了一种利用ＨＴＭＬ编写用户界面、利用交互程序实现向中药数据库中批量添加数据的方法。     

用于药物活性预报的Co-Training方法

在药物设计中,可以利用药物分子的构效关系模型进行药物活性的预报,从而降低药物开发的成本、缩短开发的周期。本文尝试结合Co-Training方法和嵌入式特征选择方法,提出了一种新的FESCOT（Feature Selection for Co-Training）算法。算法在药物活性数据集上进行了实验,结果显示结合了特征选择的Co-Training方法较之以前泛化能力有所提高。