Article

Genetic programming for human oral bioavailability of drugs

Authors:

Francesco Archetti,

Stefano Lanzeni,

Leonardo VanneschiAuthors Info & Claims

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

Pages 255 - 262

https://doi.org/10.1145/1143997.1144042

Published: 08 July 2006 Publication History

Abstract

Automatically assessing the value of bioavailability from the chemical structure of a molecule is a very important issue in biomedicine and pharmacology. In this paper, we present an empirical study of some well known Machine Learning techniques, including various versions of Genetic Programming, which have been trained to this aim using a dataset of molecules with known bioavailability. Genetic Programming has proven the most promising technique among the ones that have been considered both from the point of view of the accurateness of the solutions proposed, of the generalization capabilities and of the correlation between predicted data and correct ones. Our work represents a first answer to the demand for quantitative bioavailability estimation methods proposed in literature, since the previous contributions focus on the classification of molecules into classes with similar bioavailability.

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Cited By

Banzhaf WBakurov ILi XHandl J(2024)On the Nature of the Phenotype in Tree Genetic ProgrammingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654129(868-877)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654129
Wirasantoso MHasmawati Kurniawan I(2024)Implementation of Hybrid Bat Algorithm-Ensemble on Human Oral Bioavailability Prediction of Drug Candidate2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)10.1109/ICETSIS61505.2024.10459424(1663-1667)Online publication date: 28-Jan-2024
https://doi.org/10.1109/ICETSIS61505.2024.10459424
Ali MKshirsagar MNaredo ERyan C(2023)Dynamic Grammar Pruning for Program Size Reduction in Symbolic RegressionSN Computer Science10.1007/s42979-023-01840-y4:4Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1007/s42979-023-01840-y
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Genetic programming for human oral bioavailability of drugs

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cover image ACM Conferences

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

July 2006

2004 pages

ISBN:1595931864

DOI:10.1145/1143997

General Chair:
Mike Cattolico
Tiger Mountain Scientific, Inc., USA

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 July 2006

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July 8 - 12, 2006

Washington, Seattle, USA

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GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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https://dl.acm.org/doi/10.1145/3638529.3654129
Wirasantoso MHasmawati Kurniawan I(2024)Implementation of Hybrid Bat Algorithm-Ensemble on Human Oral Bioavailability Prediction of Drug Candidate2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)10.1109/ICETSIS61505.2024.10459424(1663-1667)Online publication date: 28-Jan-2024
https://doi.org/10.1109/ICETSIS61505.2024.10459424
Ali MKshirsagar MNaredo ERyan C(2023)Dynamic Grammar Pruning for Program Size Reduction in Symbolic RegressionSN Computer Science10.1007/s42979-023-01840-y4:4Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1007/s42979-023-01840-y
Castelli MManzoni LMariot LMenara GPietropolli G(2022)The Effect of Multi-Generational Selection in Geometric Semantic Genetic ProgrammingApplied Sciences10.3390/app1210483612:10(4836)Online publication date: 10-May-2022
https://doi.org/10.3390/app12104836
Ali MKshirsagar MNaredo ERyan CWagner M(2022)Automated grammar-based feature selection in symbolic regressionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528852(902-910)Online publication date: 8-Jul-2022
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Casadei FPappa G(2021)Multi-region symbolic regression: combining functions under a multi-objective approachNatural Computing: an international journal10.1007/s11047-021-09851-520:4(753-773)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s11047-021-09851-5
Hosseini Amini SAbdollahi MAmir Haeri M(2020)Rule-centred genetic programming (RCGP): an imperialist competitive approachApplied Intelligence10.1007/s10489-019-01601-650:8(2589-2609)Online publication date: 1-Aug-2020
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Dou TRockett P(2018)Comparison of semantic-based local search methods for multiobjective genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-018-9325-419:4(535-563)Online publication date: 1-Dec-2018
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Dick GBosman P(2017)Sensitivity-like analysis for feature selection in genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071338(401-408)Online publication date: 1-Jul-2017
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Vanneschi LCastelli MGonçalves IManzoni LSilva S(2017)Geometric semantic genetic programming for biomedical applications: A state of the art upgrade2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969311(177-184)Online publication date: 5-Jun-2017
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