Quantitative drug design

Redl, G.; tert., R. D. Cramer; Berkoff, Charles E.

doi:10.1039/cs9740300273

Cited by 45 publications

(22 citation statements)

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“…These training-set molecules are then analysed to develop a decision rule that can be used to classify new molecules (the test-set) into one of the two classes. The first application of machine learning in computer-aided molecular design (CAMD) was probably substructural analysis, which was introduced by Cramer et al in the early Seventies as a tool for the automated analysis of biological screening data [18,19]. Machine learning is now a very active area of research in computer science, with the increasing availability of large data repositories of all sorts spurring interest in the development of novel tools for data mining [20,21].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of machine-learning methods for ligand-based virtual screening

Chen

Harrison

Papadatos

et al. 2007

J Comput Aided Mol Des

113

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Abstract. Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of a naive Bayesian classifier when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.

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Section: Introductionmentioning

confidence: 99%

Evaluation of machine-learning methods for ligand-based virtual screening

Chen

Harrison

Papadatos

et al. 2007

J Comput Aided Mol Des

113

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“…An initial population of possible solutions is generated with the initial weights W 1 -W 5 being assigned by a randomnumber generator that has been primed in this simple example to generate integer weights in the range 0-10. In the example, the population contains six chromosomes, C [1][2][3][4][5][6] , and the initial population is shown in Figure 1b. Each chromosome is then used to compute the sum-of-weights for each molecule, as shown in Figure 1c.…”

Section: The Genetic Algorithmmentioning

confidence: 99%

“…Each chromosome is then used to compute the sum-of-weights for each molecule, as shown in Figure 1c. For example, M 1 contains F 2 and F 5 , so its sum-of-weights using C 1 is the sum of W 2 and W 5 , i.e., 3; using C 2 the sum is 8, and so on for C [3][4][5][6] .…”

Section: The Genetic Algorithmmentioning

confidence: 99%

Calculation of Substructural Analysis Weights Using a Genetic Algorithm

Holliday

Sani

Willett

2015

J. Chem. Inf. Model.

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“…In lots of cases it has been known that inaccuracy owing to the approximate nature of quantum-chemical procedure and ignorance of solvation effects can be transferred to a great extent within structurally associated series. As a result, despite the fact that the absolute values of computed descriptors are not directly related, the relative values can still be significant [24]. In addition, on the basis of atoms or groups, molecular wave function derived electronic descriptors can also be divided, permitting the explanation of different molecular areas individually.…”

Section: Introductionmentioning

confidence: 99%

A Concise Review on the Significance of QSAR in Drug Design

Tandon¹,

Chakraborty²,

Suhag³

2019

CBE

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Drug designing is a crucial step in the exploration of novel drugs which requires potent methodologies. One of such methodologies is Quantitative Structure Activity Relationship (QSAR) which is a widely used statistical tool that correlates the structure of a molecule to a biological activity as a function of molecular descriptors, thereby, playing an essential role in the drug designing. QSAR utilizes Density Functional Theory (DFT) based chemical descriptors for this purpose. The selection of such significant molecular descriptors from various available descriptors is the foremost challenge in a QSAR as structural descriptors are representative of the molecular characteristics accountable for the relevant activity. Recently, new QSAR approaches have been introduced which further enhance the study of the activities. Further, the constructed QSAR models also need to be tested and validated for their efficiency and practical usage. As the QSAR models are structure specific, they may not be universally applicable. However, because of their high precision and efficacy, they have a promising future in the world of drug design. This review briefly summarizes the role of descriptor based QSAR in drug design in conjunction with existing QSAR approaches and also the utility as well as constraints of this approach in drug design.

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Quantitative drug design

Cited by 45 publications

References 0 publications

Evaluation of machine-learning methods for ligand-based virtual screening

Evaluation of machine-learning methods for ligand-based virtual screening

Calculation of Substructural Analysis Weights Using a Genetic Algorithm

A Concise Review on the Significance of QSAR in Drug Design

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