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

Mochizuki, Masahiro; Suzuki, Shogo D.; Yanagisawa, Keisuke; Ohue, Masahito; Akiyama, Yutaka

doi:10.1007/s11030-018-9842-3

Cited by 15 publications

(6 citation statements)

References 18 publications

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“…Here, activity values of the training data were standardized to inhibition rates at 20 μM using Hill’s equation 28 under the assumption that the Hill coefficient is 1. Compounds with similar physicochemical properties to those of known inhibitors were extracted from the library using a modified method of QED 29,30 . Inhibition rates of these compounds were predicted with the trained model 24 .…”

Section: Methodsmentioning

confidence: 99%

A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Chiba

Ohue

Gryniukova

et al. 2019

Sci Rep

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Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

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Section: Methodsmentioning

confidence: 99%

A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Chiba

Ohue

Gryniukova

et al. 2019

Sci Rep

Self Cite

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“…In other transformation operations, it may be necessary to make improvements by utilizing distance features. • It is worthwhile to verify that this improvement is also effective for other tasks of compound supervised learning, e.g., drug-like compound filter [18], side-effect prediction [19], toxicity prediction [20], and stability prediction [21], [22].…”

Section: Assembly Of Paired Features Based On Distances On the Graphmentioning

confidence: 95%

Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph

Ohue,

Ii,

Yanagisawa

et al. 2019

Preprint

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Machine learning is often used in virtual screening to find compounds that are pharmacologically active on a target protein. The weave module is a type of graph convolutional deep neural network that uses not only features focusing on atoms alone (atom features) but also features focusing on atom pairs (pair features); thus, it can consider information of nonadjacent atoms. However, the correlation between the distance on the graph and the three-dimensional coordinate distance is uncertain. In this paper, we propose three improvements for modifying the weave module. First, the distances between ring atoms on the graph were modified to bring the distances on the graph closer to the coordinate distance. Second, different weight matrices were used depending on the distance on the graph in the convolution layers of the pair features. Finally, a weighted sum, by distance, was used when converting pair features to atom features. The experimental results show that the performance of the proposed method is slightly better than that of the weave module, and the improvement in the distance representation might be useful for compound activity prediction.

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

Cited by 15 publications

References 18 publications

A prospective compound screening contest identified broader inhibitors for Sirtuin 1

A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph

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

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