METADOCK: A parallel metaheuristic schema for virtual screening methods

Imbernón, Baldomero; Cecilia, José M.; Pérez‐Sánchez, Horacio; Giménez, Domingo

doi:10.1177/1094342017697471

Cited by 21 publications

(19 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Imbernón et al described a methodology for establishing the workload distribution on a multi‐GPU environment, which is shown in Table , second column. This would supposedly balance execution time in all GPUs, as our former policy was to assign more work to more powerful GPUs.…”

Section: Resultsmentioning

confidence: 99%

“…This is based on the principle that all metaheuristics follow similar patterns, and particularly, those based on populations share six basic functions (see Algorithm 1), ie, Initialize, End Condition, Select, Combine, Improve, and Include. Each of these functions works with various sets or populations ( S , Ssel , and Scom ) and parameters (we refer the reader to the work of Imbernón et al for insights). S represents the whole population of candidate solutions.…”

Section: Metadock: Applying Metaheuristics On Virtual Screening Methodsmentioning

confidence: 99%

“…Our previous work shows that these metaheuristics can be widely enriched with combinations of the above to derive multiple hybridations with different results and effects . This time, we consider only those three as basic building blocks and, instead, we move the set of 17 parameters to study its influence on a performance per watt basis.…”

Section: Methodsmentioning

confidence: 99%

“…When the HPC cluster assembles GPUs coming from different generations, frequencies, or memories, the number of ligand conformations assigned to each GPU should be done according to compute capabilities. In our previous work, uneven workload distributions were designed to maximize performance as a primary goal. We will use these as a departure point to illustrate that there is room for improvement when we include energy criteria to guide the partitioning process.…”

Section: Gpu Implementation For Heterogeneous Nodesmentioning

confidence: 99%

“…On the other hand, BINDSURF, and more recently, BUDE, propose the use of GPUs by dividing the whole protein surface into arbitrary independent regions (or spots). Finally, we propose M E T A D O C K , a multi GPU‐based virtual screening methodology that is based on a parameterized metaheuristic schema. In short, M E T A D O C K is able to generate a wide range of metaheuristics, like genetic algorithms, scatter, or tabu search, by setting up a group of parameters.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Energy‐based tuning of metaheuristics for molecular docking on multi‐GPUs

Pérez-Serrano

Imbernón

Cecilia

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

Summary Virtual Screening (VS) methods simulate molecular interactions in silico to look for the best chemical compound that interacts with a given molecular target. VS is becoming increasingly popular to accelerate the drug discovery process and constitute hard optimization problems with a huge computational cost. To deal with these two challenges, we have created METADOCK, an application that (1) enables a wide range of metaheuristics through a parametrized schema and (2) promotes the use of a multi‐GPU environment within a heterogeneous cluster. Metaheuristics provide approximate solutions in a reasonable time frame, but, given the stochastic nature of real‐life procedures, the energy budget goes hand in hand with acceleration to validate the proposed solution. This paper evaluates energy trade‐offs and correlations with performance for a set of metaheuristics derived from METADOCK. We establish a solid inference from minimal power to maximal performance in GPUs, and from there, to optimal energy consumption. This way, ideal heuristics can be chosen according not only to best accuracy and performance but also to energy requirements. Our study starts with a preselection of parameterized metaheuristic functions, building blocks where we will find optimal patterns from power criteria while preserving parallelism through a GPU execution. We then establish a methodology to figure out the best instances of the parameterized kernels based on energy patterns obtained, which are analyzed from different viewpoints, ie, performance, average power, and total energy consumed. We also compare the best workload distributions for optimal performance and power efficiency among Pascal and Maxwell GPUs on popular Titan models. Our experimental results demonstrate that the most power efficient GPU can be overloaded in order to reduce the total amount of energy required by as much as 20%, finding unique scenarios where Maxwell does it better in execution time, but with Pascal always ahead in performance per watt, reaching peaks of up to 40%.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Metadock: Applying Metaheuristics On Virtual Screening Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Gpu Implementation For Heterogeneous Nodesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Energy‐based tuning of metaheuristics for molecular docking on multi‐GPUs

Pérez-Serrano

Imbernón

Cecilia

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

Use of a Desktop Grid to Effectively Discover Hits in Virtual Drug Screening

Ivashko

Nikitina

2019

Communications in Computer and Information Science

View full text Add to dashboard Cite

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

et al. 2021

View full text Add to dashboard Cite

Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

show abstract

METADOCK: A parallel metaheuristic schema for virtual screening methods

Cited by 21 publications

References 51 publications

Energy‐based tuning of metaheuristics for molecular docking on multi‐GPUs

Energy‐based tuning of metaheuristics for molecular docking on multi‐GPUs

Use of a Desktop Grid to Effectively Discover Hits in Virtual Drug Screening

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Contact Info

Product

Resources

About