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Discovery of Chemical Transformations with the Use of Machine Learning [chapter]

Grzegorz Fic, Grazyna Nowak
2004 Lecture Notes in Computer Science  
They exploit different sources of knowledge and chemical reaction data for the classification, generalization, and derivation of rules in a form of the graph transformation schemes.  ...  Application of these learned rules in the course of reaction simulation enables us to predict -for a given set of reacting molecules -all possible reaction courses, having responses in real chemistry.  ...  Recent advances in the development of the CSB provide new machine learning capabilities.  ... 
doi:10.1007/978-3-540-24687-9_95 fatcat:czwwinwfdvg6fgkxqdlk6fq2ui

Predictive Power of Time-Series Based Machine Learning Models for DMPK Measurements in Drug Discovery [chapter]

Modest von Korff, Olivier Corminboeuf, John Gatfield, Sébastien Jeay, Isabelle Reymond, Thomas Sander
2019 Lecture Notes in Computer Science  
Our results give a first estimation of the power of machine learning to predict DMPK properties of compounds in an ongoing drug discovery project.  ...  A median model was used as a baseline to assess the machine learning model prediction quality.  ...  A chemical series in drug discovery starts usually with one or a few molecules, often, with modest activity on the target protein.  ... 
doi:10.1007/978-3-030-30493-5_67 fatcat:tnj27dgtujeirlmsicrurxqvv4

Machine learning in chemoinformatics and drug discovery

Yu-Chen Lo, Stefano E. Rensi, Wen Torng, Russ B. Altman
2018 Drug Discovery Today  
To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR  ...  With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound  ...  Acknowledgments We thank all members of the Helix group at Stanford University for their helpful feedback and suggestions.  ... 
doi:10.1016/j.drudis.2018.05.010 pmid:29750902 pmcid:PMC6078794 fatcat:ckxznjxuujajle6iqycgi74d7i

Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review

Guangshuai Han, Lyles School of Civil Engineering, Sustainable Materials and Renewable Technology (SMART) Lab, Purdue University, West Lafayette, IN 47906, USA, Yixuan Sun, Yining Feng, Guang Lin, Na Lu, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47906, USA, Lyles School of Civil Engineering, Sustainable Materials and Renewable Technology (SMART) Lab, Purdue University, West Lafayette, IN 47906, USA, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47906, USA, Center for intelligent infrastructure, Purdue University, West Lafayette, IN 47906, USA, Lyles School of Civil Engineering, Sustainable Materials and Renewable Technology (SMART) Lab, Purdue University, West Lafayette, IN 47906, USA, Center for intelligent infrastructure, Purdue University, West Lafayette, IN 47906, USA
2021 ES Materials & Manufacturing  
In this paper, we summarize recent progress and present the entire workflow in machine learning applications to thermoelectric material discovery, with an emphasis on machine learning regression models  ...  Machine learning has been regarded as a promising tool to facilitate material design thanks to its fast inference time.  ...  Guang Lin gratefully acknowledges the support from the National Science Foundation DMS-1555072.  ... 
doi:10.30919/esmm5f451 fatcat:2bt4knjqyngtjayhrzfxr2ezye

Perspectives on Supercomputing and Artificial Intelligence Applications in Drug Discovery

2020 Supercomputing Frontiers and Innovations  
The review ends with the perspectives on computational opportunities and challenges in drug discovery by introducing new drug design principles and modeling the process of packing DNA with histones in  ...  The evolution results in big data accumulated in life sciences and the fields of drug discovery.  ...  This paper is distributed under the terms of the Creative Commons Attribution-Non Commercial 3.0 License which permits non-commercial use, reproduction and distribution of the work without further permission  ... 
doi:10.14529/jsfi200302 fatcat:577xucm4sbdmvbg5oymt4vp2ym

Universal Chemical Synthesis and Discovery with 'The Chemputer'

Piotr S. Gromski, Jarosław M. Granda, Leroy Cronin
2019 Trends in Chemistry  
The alternative to this problem, as shown in this opinion article, is the development of an approach to universal chemistry using a programming language with automation in combination with machine learning  ...  We argue that the potential of rapidly developing technologies (e.g., machine learning and robotics) are more fully realized by operating seamlessly with the way that synthetic chemists currently work  ...  The use of machine learning allowed for autonomous exploration of reaction space allowing for discovery of four new chemical transformations [26] .  ... 
doi:10.1016/j.trechm.2019.07.004 fatcat:c7ak3odppbcyliv6mp3qa4wi6q

Transformation of Drug Discovery towards Artificial Intelligence: An in Silico Approach [chapter]

Ruby Srivastava
2021 Density Functional Theory - Recent Advances, New Perspectives and Applications [Working Title]  
With these "in silico" approaches, machines are learning and offering solutions to some of the most complex drug related problems and has well positioned them as a next frontier for potential breakthrough  ...  This chapter elaborates the crosstalk between the machine learning techniques, computational tools and the future of AI in the pharmaceutical industry.  ...  Acknowledgements The author acknowledges the financial assistance by the DST WOS-A (SR/WOS-A/CS-69/ 2018) scheme. She is also thankful to her mentor Dr.  ... 
doi:10.5772/intechopen.99018 fatcat:eyhgxoz2xrcbhiiguum5zinzgi

Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

Manish Kumar Tripathi, Abhigyan Nath, Tej P. Singh, A. S. Ethayathulla, Punit Kaur
2021 Molecular diversity  
The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space  ...  This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug discovery processes such as virtual drug screening, de  ...  Thus, with the advancement of AI methodology, the deep learning approach has been incorporated to generate new chemical entities with its powerful learning capabilities.  ... 
doi:10.1007/s11030-021-10256-w pmid:34159484 pmcid:PMC8219515 fatcat:p3lsp57x6rbnxgxdu7y5dggdeu

Machine Learning Methods in Drug Discovery

Lauv Patel, Tripti Shukla, Xiuzhen Huang, David W. Ussery, Shanzhi Wang
2020 Molecules  
In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates.  ...  increased the reliability of the machine learning and deep learning incorporated techniques.  ...  Support Vector Machine (SVM) SVMs are supervised machine learning algorithms used in drug discovery to separate classes of compounds based on the feature selector by deriving a hyperplane.  ... 
doi:10.3390/molecules25225277 pmid:33198233 fatcat:xlc7ystwjzdkveob74rvkuvfpy

Unsupervised Machine Learning Discovery of Chemical and Physical Transformation Pathways from Imaging Data [article]

Sergei V. Kalinin, Ondrej Dyck, Ayana Ghosh, Yongtao Liu, Roger Proksch, Bobby G. Sumpter, Maxim Ziatdinov
2021 arXiv   pre-print
We show that unsupervised machine learning can be used to learn physical and chemical transformation pathways from the observational microscopic data, as demonstrated for atomically resolved images in  ...  The approach encodes the information contained in image sequences using a small number of latent variables, allowing the exploration of chemical and physical transformation pathways via the latent space  ...  Discovery of molecular structural fragments and chemical transformation mechanisms via unsupervised machine learning.  ... 
arXiv:2010.09196v2 fatcat:2h6lxpyh4ve7lb5pjtqn6ynqnq

Value-Added Chemical Discovery Using Reinforcement Learning [article]

Peihong Jiang, Hieu Doan, Sandeep Madireddy, Rajeev Surendran Assary, Prasanna Balaprakash
2019 arXiv   pre-print
While some effort has been made to adapt machine learning techniques to the retrosynthesis planning problem, value-added chemical discovery presents unique challenges.  ...  With a more versatile formulation of the problem as a Markov decision process, we address the problem using deep reinforcement learning techniques and present promising preliminary results.  ...  Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research, under Contract DE-AC02-06CH11357.  ... 
arXiv:1911.07630v1 fatcat:y5crvdqba5ht5o7nq72ukbusoe

A Novel Customized Big Data Analytics Framework for Drug Discovery

A. Jainul Fathima, G. Murugaboopathi
2018 Journal of Cyber Security and Mobility  
To tackle the complexity of data and to get better insight into the data, big data analytics can be used to integrate the massive amount of pharmaceutical data and computational tools in an analytic framework  ...  This paper presents an overview of big data analytics in the field of drug discovery and outlines an analytic framework which can be applied to computational drug discovery process and briefly discuss  ...  Machine Learning Algorithms for Drug Discovery Machine learning programs enable the computer to learn from experiences and adapt their behavior.  ... 
doi:10.13052/jcsm2245-1439.7111 fatcat:3fsnej5g7zfrxij6ggbp74a3zi

Mapping biologically active chemical space to accelerate drug discovery

G. Sitta Sittampalam, Dobrila D. Rudnicki, Danilo A. Tagle, Anton Simeonov, Christopher P. Austin
2019 Nature reviews. Drug discovery  
A specialized platform for innovative research exploration -ASPIRE -in preclinical drug discovery could help study unexplored biologically active chemical space through integrating automated synthetic  ...  Furthermore, the development of technologies to integrate machine learning with auto-mated chemical synthesis is currently being funded by the Defense Advanced Research Projects Agency in the "Make-It"  ...  • The current lack of 'big data' in chemistry and the synthetic chemical space explored to date is an impediment to AI/machine learning, because these algorithms use both positive and negative data  ... 
doi:10.1038/d41573-018-00007-2 pmid:30710133 pmcid:PMC7040855 fatcat:2u6oyzvojzbqljclr25kgn3bga

A machine learning approach in predicting mosquito repellency of plant – derived compounds

Jose Isagani B. Janairo, Gerardo C. Janairo, Frumencio F. Co
2018 Nova Biotechnologica et Chimica  
In this paper, machine learning was utilized to establish association between the mosquito repellent activity of 33 natural compounds using 20 chemical descriptors.  ...  In order to fully realize the potential of natural repellents, focusing on the discovery of natural compounds eliciting repellency is of paramount importance.  ...  Acknowledgements This study was funded by the National Academy of Science and Technology, Philippines, with support from the Department of Science and Technology -Innovation Council (DOST-PCIEERD) through  ... 
doi:10.2478/nbec-2018-0006 fatcat:d4socvted5cgzhdeqxy276k2ra

The Role of Machine Learning in the Understanding and Design of Materials

Seyed Mohamad Moosavi, Kevin Maik Jablonka, Berend Smit
2020 Journal of the American Chemical Society  
Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design.  ...  face together with our perspective on the future of rational materials design and discovery.  ...  The authors declare no competing financial interest. ■ ACKNOWLEDGMENTS This  ... 
doi:10.1021/jacs.0c09105 pmid:33170678 pmcid:PMC7716341 fatcat:pxpyowcsdrbsfmegtxeftotwa4
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