Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

2021 ◽  
Vol 21 ◽  
Author(s):  
Larissa Henriques Evangelista Castro ◽  
Carlos Mauricio R. Sant'Anna
Keyword(s):  
Drug Design ◽  
Similar Efficacy ◽  
Learning Approaches ◽  
Resistance Development ◽  
Multifactorial Diseases ◽  
Target Drug ◽  
Single Target ◽  
Modeling Techniques ◽  
Computer Based ◽  
New Challenges

: Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional “one-target, one disease” paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

The Separation of Reviewing Knowledge from Medical Knowledge

1995 ◽  
Vol 34 (01/02) ◽  
pp. 131-139 ◽  
Author(s):  
M. A. Musen ◽  
J. van der Lei
Keyword(s):  
Medical Knowledge ◽  
Patient Record ◽  
Patient Records ◽  
Computer Based ◽  
New Challenges ◽  
Arden Syntax

Abstract:The developers of reviewing systems that rely on computer-based patient-record systems as a source of data need to model reviewing knowledge and medical knowledge. We simulate how the same medical knowledge could be entered in four different systems: CARE, the Arden syntax, Essential-attending and HyperCritic. We subsequently analyze how the original knowledge is represented in the symbols or syntax used by these systems. We conclude that these systems provide different alternatives in dealing with the vocabulary provided by the computer-based patient records. In addition, the use of computer-based patient records for review poses new challenges for the content of that record: to facilitate review, the reasoning of the physician needs to be captured in addition to the actions of the physician.

Recent Advances in the Rational Drug Design Based on Multi-target Ligands

2020 ◽  
Vol 27 (28) ◽  
pp. 4720-4740 ◽  
Author(s):  
Ting Yang ◽  
Xin Sui ◽  
Bing Yu ◽  
Youqing Shen ◽  
Hailin Cong
Keyword(s):  
Drug Resistance ◽  
Clinical Practice ◽  
Side Effects ◽  
Drug Design ◽  
Rational Drug Design ◽  
Health Conditions ◽  
Pharmacokinetic Parameters ◽  
Single Target ◽  
Rational Drug ◽  
Huge Challenge

Multi-target drugs have gained considerable attention in the last decade owing to their advantages in the treatment of complex diseases and health conditions linked to drug resistance. Single-target drugs, although highly selective, may not necessarily have better efficacy or fewer side effects. Therefore, more attention is being paid to developing drugs that work on multiple targets at the same time, but developing such drugs is a huge challenge for medicinal chemists. Each target must have sufficient activity and have sufficiently characterized pharmacokinetic parameters. Multi-target drugs, which have long been known and effectively used in clinical practice, are briefly discussed in the present article. In addition, in this review, we will discuss the possible applications of multi-target ligands to guide the repositioning of prospective drugs.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1651-1660
Author(s):  
Anuraj Nayarisseri
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Binding Affinity ◽  
Chemical Biology ◽  
De Novo ◽  
Structure Based Drug Design ◽  
Computational Approaches ◽  
Drug Designing ◽  
Traditional Drug ◽  
Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

Developing Engineering Students' Communication and Information Retrieval Skills Utilizing Capstone Projects

2015 ◽  
Vol 4 (3) ◽  
pp. 1-20 ◽  
Author(s):  
Aaron S. Blicblau ◽  
Jamal Naser
Keyword(s):  
Information Retrieval ◽  
International Students ◽  
Communication Skills ◽  
Teaching And Learning ◽  
Engineering Students ◽  
Learning Approaches ◽  
The World ◽  
Computer Based ◽  
Capstone Projects ◽  
Key Aspects

The pedagogy of engineering requires a better understanding of the requirements of students' abilities to learning the skills necessary for working in the engineering community. In many engineering courses around the world, one of the key aspects required of the students is that they complete an independent project in their final year of studies incorporating information retrieval and subsequent communication skills. The current work provides details teaching and learning approaches to enhance student abilities and expertise involving research skills, communication skills, and information retrieval integrated within capstone projects. Findings from this the work indicated that both domestic and international students benefited from the intensive tutorial activities involving computer based information retrieval skills. The implementation of active tutorial sessions resulted in increased grades for the majority of students, highlighting the importance of intensive active learning events for final year capstone engineering students.

Aggregation for Predictive Modeling with Relational Data

2011 ◽  
pp. 33-38
Author(s):  
Claudia Perlich ◽  
Foster Provost
Keyword(s):  
Data Mining ◽  
Predictive Modeling ◽  
Relational Databases ◽  
Feature Vector ◽  
Relational Data ◽  
Government Organizations ◽  
Modeling Techniques ◽  
Aggregation Of Information ◽  
New Challenges

Most data mining and modeling techniques have been developed for data represented as a single table, where every row is a feature vector that captures the characteristics of an observation. However, data in most domains are not of this form and consist of multiple tables with several types of entities. Such relational data are ubiquitous; both because of the large number of multi-table relational databases kept by businesses and government organizations, and because of the natural, linked nature of people, organizations, computers, and etc. Relational data pose new challenges for modeling and data mining, including the exploration of related entities and the aggregation of information from multi-sets (“bags”) of related entities.

Social or Group Learning Theories

2011 ◽  
pp. 1630-1633
Author(s):  
Gary A. Berg
Keyword(s):  
Distance Learning ◽  
Learning Process ◽  
Group Learning ◽  
Social Activity ◽  
Learning Theories ◽  
Learning Approaches ◽  
Self Reliance ◽  
Educational Environments ◽  
Computer Based ◽  
Learning Institutions

In both computer-based and traditional educational environments, there has been a growing organization of learning in groups with an increased use of teams and group projects (Berg, 2003). Goldman (1999) claims that traditionally education is seen as an activity of isolated thinkers pursuing truth in a spirit of American self-reliance. However, in practice education is very much a social activity, especially the research component that is heavily dependent on colleagues. In fact, some argue that the key to the learning process as a whole is the interaction among students, and between faculty and students (Palloff & Pratt, 1999). Group learning approaches have been widely adopted by many of the leading distance learning institutions, and consequently an understanding of this approach is important.

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