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Mol-CycleGAN: a generative model for molecular optimization

Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Tomasz Danel, Michał Warchoł
<span title="2020-01-08">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5aubiwi6v5beng6iqzj577kiaa" style="color: black;">Journal of Cheminformatics</a> </i> &nbsp;
To improve the compound design process, we introduce Mol-CycleGAN-a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones.  ...  Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property.  ...  Acknowledgements We would like to thank Sabina Podlewska for her helpful comments and for fruitful discussions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s13321-019-0404-1">doi:10.1186/s13321-019-0404-1</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33431006">pmid:33431006</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zhocx6tl3bfvvaborcs7sch464">fatcat:zhocx6tl3bfvvaborcs7sch464</a> </span>
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Mol-CycleGAN - A Generative Model for Molecular Optimization [chapter]

Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Michał Warchoł
<span title="">2019</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
To improve this process, we introduce Mol-CycleGAN -a CycleGAN-based model that generates compounds optimized for a selected property, while aiming to retain the already optimized ones.  ...  In the task of constrained optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.  ...  Conclusions In this work, we introduce Mol-CycleGAN -a new model based on CycleGAN which can be used for the de novo generation of molecules.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-30493-5_77">doi:10.1007/978-3-030-30493-5_77</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/t36xvxezdzbkriycwkjgnojmt4">fatcat:t36xvxezdzbkriycwkjgnojmt4</a> </span>
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Mol-CycleGAN - a generative model for molecular optimization [article]

Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Michał Warchoł
<span title="2019-02-06">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To augment the compound design process we introduce Mol-CycleGAN - a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones.  ...  Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property.  ...  Acknowledgement We would like to thank Sabina Podlewska for her helpful comments and for fruitful discussions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1902.02119v1">arXiv:1902.02119v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bv5ukbl2ivhr7mdigfze7lslbe">fatcat:bv5ukbl2ivhr7mdigfze7lslbe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191022214301/https://arxiv.org/pdf/1902.02119v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/90/c9/90c97804c31287a35212538e85c5f34c0424341d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1902.02119v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

MERMAID: an open source automated hit-to-lead method based on deep reinforcement learning

Daiki Erikawa, Nobuaki Yasuo, Masakazu Sekijima
<span title="2021-11-27">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5aubiwi6v5beng6iqzj577kiaa" style="color: black;">Journal of Cheminformatics</a> </i> &nbsp;
In this study, we have developed a SMILES-based generative model that can be generated starting from a certain molecule.  ...  We validated our method using a molecule dataset obtained from the ZINC database and successfully generated molecules that were both well optimized for the objectives of the quantitative estimate of drug-likeness  ...  Acknowledgements This work was partially supported by the Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s13321-021-00572-6">doi:10.1186/s13321-021-00572-6</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34838134">pmid:34838134</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8626955/">pmcid:PMC8626955</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lwc3z5ttyfbxlpvbpakdnwjwby">fatcat:lwc3z5ttyfbxlpvbpakdnwjwby</a> </span>
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Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design

Eugene Lin, Chieh-Hsin Lin, Hsien-Yuan Lane
<span title="2020-07-16">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dstyyzbt45gknhqqjsh45p55h4" style="color: black;">Molecules</a> </i> &nbsp;
Moreover, we outline the limitations in regard to the previous drug design and discovery studies using GAN models. Finally, we present a discussion of directions and challenges for future research.  ...  In this review, we focus on the latest developments for three particular arenas in drug design and discovery research using deep learning approaches, such as generative adversarial network (GAN) frameworks  ...  [44] implemented a deep learning GAN architecture called the Mol-CycleGAN structure to produce optimized molecular compounds where their molecular structures were highly similar to the original ones  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/molecules25143250">doi:10.3390/molecules25143250</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32708785">pmid:32708785</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rrik322g6vbetaubwjb3rtvajm">fatcat:rrik322g6vbetaubwjb3rtvajm</a> </span>
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From Big Data to Artificial Intelligence: chemoinformatics meets new challenges

Igor V. Tetko, Ola Engkvist
<span title="2020-12-18">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5aubiwi6v5beng6iqzj577kiaa" style="color: black;">Journal of Cheminformatics</a> </i> &nbsp;
The increasing volume of biomedical data in chemistry and life sciences requires development of new methods and approaches for their analysis.  ...  The methods developed in these studies are general ones and can be used to enhance other GMs such as scaffold decoration [18] or Mol-CycleGAN [19] .  ...  The second group of articles deals with novel machine-learning algorithms such as the use of generative models (GMs) for molecular de novo design in drug discovery.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s13321-020-00475-y">doi:10.1186/s13321-020-00475-y</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33339533">pmid:33339533</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3bpwkzpzxfbo5i4psmdawp7xsq">fatcat:3bpwkzpzxfbo5i4psmdawp7xsq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201221063557/https://jcheminf.biomedcentral.com/track/pdf/10.1186/s13321-020-00475-y.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5d/81/5d8132f52b706357b4634775415c97a382c9ecb9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s13321-020-00475-y"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Deep Graph Generators: A Survey

Faezeh Faez, Yassaman Ommi, Mahdieh Soleymani Baghshah, Hamid R. Rabiee
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
novel molecular structures to modeling social networks.  ...  Deep generative models have achieved great success in areas such as image, speech, and natural language processing in the past few years.  ...  Mol-CycleGAN [69] establishes structural similarity between the source and target molecular graphs by adopting a CycleGAN-based [126] approach.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3098417">doi:10.1109/access.2021.3098417</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6xzg5cs75zhovdbpjkignfr3xu">fatcat:6xzg5cs75zhovdbpjkignfr3xu</a> </span>
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An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design [article]

Yi Zhang
<span title="2021-10-10">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Concretely, 13 of them leverage GNN for molecular property prediction and 29 of them use RL and/or deep generative models for molecule generation and optimization.  ...  Moreover, 60 additional applications of AI in molecule generation and optimization are briefly summarized in a table.  ...  Mol-CycleGAN (Maziarka et al., 2020). Mol-CycleGAN is a GAN-based model that optimizes molecules while keeping them similar to the starting molecules.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.05478v1">arXiv:2110.05478v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uq2lera3znhpziuig6n44uuaby">fatcat:uq2lera3znhpziuig6n44uuaby</a> </span>
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Learning to Discover Medicines [article]

Tri Minh Nguyen, Thin Nguyen, Truyen Tran
<span title="2022-02-14">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
reasoning where we predict molecular properties and their binding, optimize existing compounds, generate de novo molecules, and plan the synthesis of target molecules; and (c) knowledge-based reasoning  ...  We organize the vast and rapidly growing literature of AI for drug discovery into three relatively stable sub-areas: (a) representation learning over molecular sequences and geometric graphs; (b) data-driven  ...  Molecular optimization and generation The second question is what kind of molecules interact with the given target.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.07096v1">arXiv:2202.07096v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u77zls6hezffbkmm3zy2rhnueu">fatcat:u77zls6hezffbkmm3zy2rhnueu</a> </span>
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Evaluation of a deep learning–based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning

Yingzi Liu, Yang Lei, Yinan Wang, Ghazal Shafai-Erfani, Tonghe Wang, Sibo Tian, Pretesh Patel, Ashesh B. Jani, Mark McDonald, Walter J Curran, Tian Liu, Jun Zhou (+1 others)
<span title="2019-09-05">2019</span> <i title="IOP Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zuggg3zvsfaatpw6invuoevl7a" style="color: black;">Physics in Medicine and Biology</a> </i> &nbsp;
The purpose of this work is to validate the application of a deep learning-based method for pelvic synthetic CT (sCT) generation that can be used for prostate proton beam therapy treatment planning.  ...  A cohort of 17 patients with co-registered CT and MR pairs were used to test the deep learning-based sCT generation method by leave-one-out cross-validation.  ...  Finally, a nano-water droplet was placed on the outside surface of the [DMIM]PF 6 molecular layer. e COMPASS [38] (condensed-phase-optimized molecular potentials for the atomistic simulation studies)  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1361-6560/ab41af">doi:10.1088/1361-6560/ab41af</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31487698">pmid:31487698</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7765705/">pmcid:PMC7765705</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ejb6efwqrnc2xahkirbp5yw6ea">fatcat:ejb6efwqrnc2xahkirbp5yw6ea</a> </span>
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AI for Chemical Space Gap Filling and Novel Compound Generation [article]

Monee Y. McGrady, Sean M. Colby, Jamie R Nuñez, Ryan S. Renslow, Thomas O. Metz
<span title="2022-01-28">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
With DarkChem's design, distance in this latent space is often associated with compound similarity, making sparse regions interesting targets for compound generation due to the possibility of generating  ...  We have used one such tool, a deep-learning software called DarkChem, which learns a representation of the molecular structure of compounds by compressing them into a latent space.  ...  examples of such tools are Mol-CycleGAN, 24 a generative model that creates compounds with a similar structure to the input compound but with optimized property values for increased efficacy against target  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.12398v1">arXiv:2201.12398v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/acujjbrytnhffm6aijsmvllg5y">fatcat:acujjbrytnhffm6aijsmvllg5y</a> </span>
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Generative chemistry: drug discovery with deep learning generative models [article]

Yuemin Bian, Xiang-Qun Xie
<span title="2020-08-20">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for the generative chemistry.  ...  From the generation of original texts, images, and videos, to the scratching of novel molecular structures, the incredible creativity of deep learning generative models surprised us about the height machine  ...  Łukasz Maziarka et al. introduced Mol-CycleGAN for derivatives design and compound optimization 115 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.09000v1">arXiv:2008.09000v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ivznoc4bsbfoderwr2ted76fiq">fatcat:ivznoc4bsbfoderwr2ted76fiq</a> </span>
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Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design [article]

Xiufeng Yang and Tanuj Kr Aasawat and Kazuki Yoshizoe
<span title="2021-04-06">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, while massively parallel computing is often used for training models, it is rarely used for searching solutions for combinatorial optimization problems.  ...  In this paper, we propose a novel massively parallel Monte-Carlo Tree Search (MP-MCTS) algorithm that works efficiently for 1,000 worker scale, and apply it to molecular design.  ...  use deep generative models (to generate candidate molecules), followed by optimization algorithms to focus on the promising candidates having desired molecular property (mainly Bayesian Optimization (  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.10504v3">arXiv:2006.10504v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xbo2c4rcsnghlaciwg3m5jf6my">fatcat:xbo2c4rcsnghlaciwg3m5jf6my</a> </span>
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A Binded VAE for Inorganic Material Generation [article]

Fouad Oubari, Antoine de Mathelin, Rodrigue Décatoire, Mathilde Mougeot
<span title="2021-12-17">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We show on a real issue of rubber compound design that the proposed approach outperforms the standard generative models which opens new perspectives for material design optimization.  ...  Furthermore, traditional generative model validation processes as visual verification, FID and Inception scores are tailored for images and cannot then be used as such in this context.  ...  Mol-cyclegan: a generative model for molecular optimization. Journal of Cheminformatics, 12(1):1–18, 2020. [16] Reuben R Shamir, Yuval Duchin, Jinyoung Kim, Guillermo Sapiro, and Noam Harel.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.09570v1">arXiv:2112.09570v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gqussf4g4jbz3c4xtqvimny23q">fatcat:gqussf4g4jbz3c4xtqvimny23q</a> </span>
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Proceedings of the World Molecular Imaging Congress 2021, October 5-8, 2021: General Abstracts

<span title="2022-01-04">2022</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nes4x2ysovbixokk2bgxvvkzhi" style="color: black;">Molecular Imaging and Biology</a> </i> &nbsp;
In general, the values of CNR reached a plateau at around 8 iterations with an average improvement factor of about 1.7 for processed MRI images.  ...  For most clinical MRI cases, the total number of iterations for enhanced image quality is around 8 with a total number of resolution subsets around 4.  ...  was superior over the small molecular counterpart for prostate cancer therapy.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11307-021-01693-y">doi:10.1007/s11307-021-01693-y</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34982365">pmid:34982365</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8725635/">pmcid:PMC8725635</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4sfb3isoyfdhfbiwxfr55gvqym">fatcat:4sfb3isoyfdhfbiwxfr55gvqym</a> </span>
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