The inevitable QSAR renaissance

Cramer, Richard D.

doi:10.1007/s10822-011-9495-0

Cited by 66 publications

(42 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since then, such predictive modelling approaches have grown to become a core part of the drug discovery process (Cumming et al 2013;Cherkasov et al 2014). The subject is still increasing in importance (Cramer 2012). This may be attributed to the alignment of a number of factors, including increased availability of data, advances in data-mining methodologies as well as a more widespread appreciation of how to avoid many of the numerous pitfalls in building and applying QSAR models (Cherkasov et al 2014).…”

Section: Discussionmentioning

confidence: 99%

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

et al. 2017

View full text Add to dashboard Cite

We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance, as it is a key step in the development of new medicines. The standard QSAR learning problem Learn (2018) 107:285-311 is: given a target (usually a protein) and a set of chemical compounds (small molecules) with associated bioactivities (e.g. inhibition of the target), learn a predictive mapping from molecular representation to activity. Although almost every type of machine learning method has been applied to QSAR learning there is no agreed single best way of learning QSARs, and therefore the problem area is well-suited to meta-learning. We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 3 molecular representations, applied to more than 2700 QSAR problems. (These results have been made publicly available on OpenML and represent a valuable resource for testing novel meta-learning methods.) We then investigated the utility of algorithm selection for QSAR problems. We found that this meta-learning approach outperformed the best individual QSAR learning method (random forests using a molecular fingerprint representation) by up to 13%, on average. We conclude that meta-learning outperforms base-learning methods for QSAR learning, and as this investigation is one of the most extensive ever comparisons of base and meta-learning methods ever made, it provides evidence for the general effectiveness of meta-learning over base-learning.

show abstract

Section: Discussionmentioning

confidence: 99%

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

et al. 2017

View full text Add to dashboard Cite

show abstract

“…47 Desde o final dos anos 1990, métodos usados em QSAR/QSPR apresentaram vários avanços e crescente interesse de grupos de pesquisa e indústrias farmacêuticas. 82 Inúmeros descritores moleculares, métodos de aprendizado de máquina e parâmetros de validação foram desenvolvidos e vêm sendo aplicados. Os estudos de QSAR/ QSPR consolidaram a quimioinformática como uma ciência capaz de transformar a informação química em conhecimento.…”

Section: Relações Quantitativas Entre Estrutura Química E Atividade/punclassified

Quimioinformática: Uma Introdução

Alves¹,

Braga²,

Muratov³

et al. 2017

Quim. Nova

View full text Add to dashboard Cite

publicado na web em 08/11/2017 CHEMINFORMATICS: AN INTRODUCTION. Cheminformatics is an interdisciplinary field between chemistry and informatics, which has evolved considerably since its inception in the 1960s. Initially, the cheminformatics community dealt primarily with practical and technical aspects of chemical structure representation, manipulation, and processing, while modern research explores a new role: the exploration and interpretation of large chemical databases and the discovery of new compounds with desired activity and safety profiles. Despite the recent release of several hallmark reviews addressing methods and application of cheminformatics written in Portuguese, so far there are no scientific articles presenting cheminformatics research to the Brazilian scientific community yet. To address this gap, we aim to introduce the field of cheminformatics to both students and researchers in a simple and didactic way by narrating important historical facts and contextualizing information within the scope of various applications.Keywords: cheminformatics; QSAR; chemical similarity; structure representation, property prediction; virtual screening. INTRODUÇÃOA quimioinformática é uma ciência interdisciplinar que utiliza recursos das ciências da computação e informação para resolver problemas da química.1 O termo quimioinformática foi cunhado por Frank Brown em 1998, definindo-a como "mistura de recursos de informação para transformar dados em informação e informação em conhecimento, no intuito de tomar decisões melhores e mais rápidas na área de identificação e otimização de compostos líderes".2 Em uma definição mais abrangente, em 1999, Greg Paris, então pesquisador da companhia farmacêutica Novartis, definiu a quimioinformática como "um termo genérico que engloba a concepção, criação, organização, gestão, recuperação, análise, disseminação, visualização e utilização de informação química". Sciences (1975Sciences ( -2005, chegando no nome atual em 2005 (http://pubs.acs.org/toc/jcisd8/current). Nesse intervalo, várias áreas que hoje compõem a quimioinformática foram se consolidando como (i) representação, visualização, manipulação e processamento de estruturas químicas, (ii) organização de bases de dados de estruturas químicas e (iii) estudos das relações quantitativas entre estrutura e atividade/propriedade (QSAR/QSPR, do inglês, quantitative structure-activity/property relationships). 5O campo da quimioinformática evoluiu bastante, passando de aspectos práticos e técnicas de representação, manipulação e processamento de estruturas químicas individuais até o seu papel primordial na atualidade: exploração de bases de dados químicas e descoberta de novos compostos com atividade e/ou propriedade desejadas. Ao se explorar bases de dados é possível extrair várias informações que auxiliam na compreensão do comportamento de determinado grupo de compostos e é possível gerar modelos computacionais que são utilizados para predizer a atividade de moléculas que carecem de dados experimentais, ou seja, dados de ensai...

show abstract

“…Therefore, multipotent brain permeable drugs affecting few brain targets involved in the disease pathology, such as MAO and ChE enzymes, Aβ generation/aggregation and iron accumulations were extensively studied as essential therapeutic approach in treatment of AD [28,[34][35][36][37][38][39][40][41][42][43]." Quantitative Structure Activity Relationship (QSAR) modeling and related cheminformatic methods are developed and applied in helping to guide computer-aideddrug-design (CADD) [44,45] and in polypharmacology for design of ligands with unique polypharmacological profiles [8,46]. Design of compounds with unique polypharmacology and optimal ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profile involve several steps such as: formation of chemical analogues of a lead, predicting their binding profiles using a group of ligand-based QSAR models, and synthesizing the most promising candidates with the preferred multitarget activities [8][9][10].…”

Section: Polypharmacologymentioning

confidence: 99%

Polypharmacology of dopamine D1-like receptor antagonists

Nikolic¹,

Filipić²,

Agbaba³

2015

Arh farmaciju

View full text Add to dashboard Cite

Drug discovery based on development of selective ligands for a specific target intended to modulate its activity and revert pathophysiological process is now recognized as too simplistic to design effective agent for complex multifactorial diseases, characterized by diverse physiological dysfunctions caused by deregulations of complex networks of proteins. Major challenge in modern drug discovery is to rationally design multitarget drugs able to specifically modulate only a group of desired targets while minimizing interactions with off-targets. Multifactorial cerebral mechanisms implicated in mental (psychiatrics) and neurodegenerative diseases and interactions of the neurotransmitter systems are two main reasons for applying polypharmacology ("multi-target") strategy in drug discovery for these complex brain diseases. In this paper we review polypharmacological profile and potential therapeutic application of dopamine D 1 -like receptor antagonists.

show abstract

The inevitable QSAR renaissance

Abstract: QSAR approaches, including recent advances in 3D-QSAR, are advantageous during the lead optimization phase of drug discovery and complementary with bioinformatics and growing data accessibility. Hints for future QSAR practitioners are also offered.

Cited by 66 publications

References 33 publications

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

Quimioinformática: Uma Introdução

Polypharmacology of dopamine D1-like receptor antagonists

Contact Info

Product

Resources

About