Novel opportunities for computational biology and sociology in drug discovery

Yao, Lixia; Evans, James A.; Rzhetsky, Andrey

doi:10.1016/j.tibtech.2009.06.003

Cited by 20 publications

(15 citation statements)

References 58 publications

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“…Both objectives need to process the large data set of protein structures available in biological databases such as PDB (Berman, et al, 2000) and also derived from genomic data using techniques as homology modeling (Sanchez & Sali, 1998). Screenings in lab and compound optimization are expensive and slow methods (Yao, et al, 2009), but bioinformatics can vastly help clinical research for the mentioned purposes by providing prediction of the toxicity of drugs and activity in non-tested targets, and by evolving discovered active compounds into drugs for the clinical trials.…”

Section: Introductionmentioning

confidence: 99%

Improving drug discovery using hybrid softcomputing methods

Pérez‐Sánchez

Cano

García-Rodríguez

2014

Applied Soft Computing

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Abstract. Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.

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Section: Introductionmentioning

confidence: 99%

Improving drug discovery using hybrid softcomputing methods

Pérez‐Sánchez

Cano

García-Rodríguez

2014

Applied Soft Computing

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“… Drug discovery: This is impossible without sophisticated modeling and computation. Computational methods have been successfully applied throughout the drug discovery process, from clinical data to building network models of molecular processes [7].…”

Section: Applicationsmentioning

confidence: 99%

Computational Biology

Sadiku¹,

Wang²,

Cui³

et al. 2018

IJARCSSE

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Computation is an integral part of a larger revolution that will affect how science is conducted. Computational biology is an important emerging field of biology which is uniquely enabled by computation. It involves using computers to model biological problems and interpret data, especially problems in evolutionary and molecular biology. The application of computational tools to all areas of biology is producing excitements and insights into biological problems too complex for conventional approaches. This paper provides a brief introduction on computational biology.Key Words: computational biology, computational molecular biology, bioinformatics I. INTRODUCTIONWith the advent of digital computers, several fields have discovered the potential of employing computers' capacities in storage, computation, and simulation. The computation era has exploded and generated new research areas that go beyond all expectations. One such area is computational biology [1]. Several aspects of biology cannot be studied without the power of computers. Several algorithms of specific interest to biologists, such as the BLAST (developed by Stephen Altschul in 1990) and the FASTA (developed by William Pearson in 1988) could not be applied without computers. It has been argued that computational thinking and computational methods are so crucial to understanding life that today all biology is regarded as computational biology. Today, biological knowledge is defined, organized, and accessed through computation [2].Computational biology is the science of using biological data to develop models to understand among various biological systems. It is not to be confused with biological computation, which is a branch of computer science that employs biology to build computers. It is similar to bioinformatics which is an interdisciplinary science that uses computers to process biological data. It may also be regarded as a subfield of evolutionary computation [3]. Computational biology spans several classical areas such as biology, chemistry, physics, mathematics, and computer science.

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“…Uses of sociological studies have several advantages in drug development [73]. The biotechnological and genetic engineering methods also play important role in pharmaceutical industry which is responsible for final marketization of newly discovered drugs along with other different kind of tasks.…”

Section: Contribution Of Different Disciplines In Caddmentioning

confidence: 99%