PKRank: a novel learning-to-rank method for ligand-based virtual screening using pairwise kernel and RankSVM

Suzuki, Shogo D.; Ohue, Masahito; Akiyama, Yutaka

doi:10.1007/s10015-017-0416-8

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…An advantage of QEX is that its model is only trained with a dataset of active compounds. In other words, QEX does not require a dataset of inactive compounds, which are often difficult to obtain in large numbers from public databases [7, 8]. If the examples of inactive compounds provided are insufficient, the performance of the machine learning classifier would be worse.…”

Section: Resultsmentioning

confidence: 99%

QEX: target-specific druglikeness filter enhances ligand-based virtual screening

et al. 2018

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Druglikeness is a useful concept for screening drug candidate compounds. We developed QEX, which is a new druglikeness index specific to individual targets. QEX is an improvement of the quantitative estimate of druglikeness (QED) method, which is a popular quantitative evaluation method of druglikeness proposed by Bickerton et al. QEX models the physicochemical properties of compounds that act on each target protein based on the concept of QED modeling physicochemical properties from information on US Food and Drug Administration-approved drugs. The result of the evaluation of PubChem assay data revealed that QEX showed better performance than the original QED did (the area under the curve value of the receiver operating characteristic curve improved by 0.069-0.236). We also present the c-Src inhibitor filtering results of the QEX constructed using Src family kinase inhibitors as a case study. QEX distinguished the inhibitors and non-inhibitors better than QED did. QEX works efficiently even when datasets of inactive compounds are unavailable. If both active and inactive compounds are present, QEX can be used as an initial filter to enhance the screening ability of conventional ligand-based virtual screenings.

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Section: Resultsmentioning

confidence: 99%

QEX: target-specific druglikeness filter enhances ligand-based virtual screening

et al. 2018

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“…Moreover, different algorithms and tools have been developed for LBVS such as SwissSimilarity ( http://www.swisssimilarity.ch/ ) [ 198 ], METADOCK [ 199 ], Open-source platform [ 200 ], HybridSim-VS ( http://www.rcidm.org/HybridSim-VS/ ) [ 201 ], PKRank [ 202 ], PyGOLD ( http://www.agkoch.de/ ) [ 203 ], BRUSELAS ( http://bio-hpc.eu/software/Bruselas ) [ 204 ], RADER ( http://rcidm.org/rader/ ) [ 205 ], QEX [ 206 ], IVS2vec ( https://github.com/haiping1010/IVS2Vec ) [ 207 ], AutoDock Bias ( http://autodockbias.wordpress.com/ ) [ 208 ], Ligity [ 209 ], D3Similarity ( https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php ) [ 210 ], and GCAC ( http://ccbb.jnu.ac.in/gcac ) [ 211 ]. Emerging evidence suggests the potential implementation of AI algorithms in LBVS such as identification of aurora kinase A inhibitors [ 212 ], G-quadruplex-targeting chemotypes [ 213 ], PI3Kα inhibitors [ 214 ], targeting dengue virus non-structural protein 3 helicases [ 215 ], potential selective histone deacetylase 8 inhibitors [ 216 ], and novel p-Hydroxyphenylpyruvate dioxygenase inhibitors [ 217 ].…”

Section: Applications Of Artificial Intelligence In Drug Development mentioning

confidence: 99%

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

et al. 2021

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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...

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“…Taking into consideration the complexity of feature redundancy in traditional dense list-wise Learning to Rank method and local optimum in parameter learning. The Expert list-wise ranking algorithm can be a great approach of ranking programmers as the feature dimension reduction can be achieved by the feature threshold from the loss-control function of sparse learning algorithm [10]. List-wise Neural Raking Models can be a unique approach for the coder's ranking.…”

Section: Ranking Models and Techniquesmentioning

confidence: 99%

Relating Reported Speedup to Attainable Speedup in HPC Applications Based on a Programmer's Expertise

Shuvo¹,

Ahmed²

2022

IJRP

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High Performance Computing (HPC) refers to the processing of complex computations using enhanced computing power for quick results and better accuracy. The parallel computing features of supercomputers provide an application program to obtain significant speedup over the serial implementation of certain problems in scientific computing. An HPC application largely relies on human expertise i.e., knowledge of the underlying architecture, algorithm used to solve the problem, his expertise in parallelizing the sequential code efficiently. Hence the speedup is largely dependent on the programmer's expertise. Therefore, an obtained speedup of a certain HPC application, may not always be the best possible speedup that is potentially possible on that supercomputer. So, there is always a need of finding out if the obtained speedup is the highest possible speedup for a given problem and on a given supercomputer architecture. That's why it's important to objectively rate a programmer's ability to relate it to his capability to parallelize a given code. Although, online judge platforms have given ratings to programmer's when they are tied to online judges (such as-Topcoder, Codeforce and Codechef), types of contests (long, short, national-level, international level and based on problem categories), and the programmers' sustained interest in participating (frequency of participation), problems solved, problems attempted, maturity (in years and capability) and other factors. The ranking could also be misleading in some cases; for example, in solving a difficult problem, a relatively new programmer may climb to a top position because of his familiarity with the category of the problem although he may have less submissions and experience. On the other hand, an experienced programmer can see a drop in his rank if he under-performs in one or more contests. Hence, there is no generalized way to rank a programmer based on a rating provided by an online judge platform. Therefore, we need a more sophisticated and reliable ranking model that can help us to evaluate the actual rating of a programmer. This work will involve researching existing ranking algorithms and develop a model that establishes a relationship between Programmers grade or level and the Maximum Attainable Speedup in a particular HPC application.

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PKRank: a novel learning-to-rank method for ligand-based virtual screening using pairwise kernel and RankSVM

Cited by 10 publications

References 26 publications

QEX: target-specific druglikeness filter enhances ligand-based virtual screening

QEX: target-specific druglikeness filter enhances ligand-based virtual screening

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

Relating Reported Speedup to Attainable Speedup in HPC Applications Based on a Programmer's Expertise

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