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Fromer JC, Coley CW. Computer-aided multi-objective optimization in small molecule discovery. PATTERNS (NEW YORK, N.Y.) 2023; 4:100678. [PMID: 36873904 PMCID: PMC9982302 DOI: 10.1016/j.patter.2023.100678] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Molecular discovery is a multi-objective optimization problem that requires identifying a molecule or set of molecules that balance multiple, often competing, properties. Multi-objective molecular design is commonly addressed by combining properties of interest into a single objective function using scalarization, which imposes assumptions about relative importance and uncovers little about the trade-offs between objectives. In contrast to scalarization, Pareto optimization does not require knowledge of relative importance and reveals the trade-offs between objectives. However, it introduces additional considerations in algorithm design. In this review, we describe pool-based and de novo generative approaches to multi-objective molecular discovery with a focus on Pareto optimization algorithms. We show how pool-based molecular discovery is a relatively direct extension of multi-objective Bayesian optimization and how the plethora of different generative models extend from single-objective to multi-objective optimization in similar ways using non-dominated sorting in the reward function (reinforcement learning) or to select molecules for retraining (distribution learning) or propagation (genetic algorithms). Finally, we discuss some remaining challenges and opportunities in the field, emphasizing the opportunity to adopt Bayesian optimization techniques into multi-objective de novo design.
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Affiliation(s)
- Jenna C Fromer
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
| | - Connor W Coley
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA.,Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
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2
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Cerruela-García G, Cuevas-Muñoz JM, García-Pedrajas N. Graph-Based Feature Selection Approach for Molecular Activity Prediction. J Chem Inf Model 2022; 62:1618-1632. [PMID: 35315648 PMCID: PMC9006223 DOI: 10.1021/acs.jcim.1c01578] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
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In the construction
of QSAR models for the prediction of molecular
activity, feature selection is a common task aimed at improving the
results and understanding of the problem. The selection of features
allows elimination of irrelevant and redundant features, reduces the
effect of dimensionality problems, and improves the generalization
and interpretability of the models. In many feature selection applications,
such as those based on ensembles of feature selectors, it is necessary
to combine different selection processes. In this work, we evaluate
the application of a new feature selection approach to the prediction
of molecular activity, based on the construction of an undirected
graph to combine base feature selectors. The experimental results
demonstrate the efficiency of the graph-based method in terms of the
classification performance, reduction, and redundancy compared to
the standard voting method. The graph-based method can be extended
to different feature selection algorithms and applied to other cheminformatics
problems.
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Affiliation(s)
- Gonzalo Cerruela-García
- Department of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, E-14071 Córdoba, Spain
| | - José Manuel Cuevas-Muñoz
- Department of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, E-14071 Córdoba, Spain
| | - Nicolás García-Pedrajas
- Department of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, E-14071 Córdoba, Spain
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3
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Assis LC, de Castro AA, de Jesus JPA, Nepovimova E, Kuca K, Ramalho TC, La Porta FA. Computational evidence for nitro derivatives of quinoline and quinoline N-oxide as low-cost alternative for the treatment of SARS-CoV-2 infection. Sci Rep 2021; 11:6397. [PMID: 33737545 PMCID: PMC7973710 DOI: 10.1038/s41598-021-85280-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 02/18/2021] [Indexed: 12/20/2022] Open
Abstract
A new and more aggressive strain of coronavirus, known as SARS-CoV-2, which is highly contagious, has rapidly spread across the planet within a short period of time. Due to its high transmission rate and the significant time–space between infection and manifestation of symptoms, the WHO recently declared this a pandemic. Because of the exponentially growing number of new cases of both infections and deaths, development of new therapeutic options to help fight this pandemic is urgently needed. The target molecules of this study were the nitro derivatives of quinoline and quinoline N-oxide. Computational design at the DFT level, docking studies, and molecular dynamics methods as a well-reasoned strategy will aid in elucidating the fundamental physicochemical properties and molecular functions of a diversity of compounds, directly accelerating the process of discovering new drugs. In this study, we discovered isomers based on the nitro derivatives of quinoline and quinoline N-oxide, which are biologically active compounds and may be low-cost alternatives for the treatment of infections induced by SARS-CoV-2.
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Affiliation(s)
- Letícia C Assis
- Department of Chemistry, Federal University of Lavras, Lavras, Minas Gerais, CEP 37200-000, Brazil
| | - Alexandre A de Castro
- Department of Chemistry, Federal University of Lavras, Lavras, Minas Gerais, CEP 37200-000, Brazil
| | - João P A de Jesus
- Laboratório de Nanotecnologia E Química Computacional, Universidade Tecnológica Federal Do Paraná, Londrina, PR, 86036-370, Brazil
| | - Eugenie Nepovimova
- Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Králové, Czech Republic
| | - Kamil Kuca
- Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Králové, Czech Republic.
| | - Teodorico C Ramalho
- Department of Chemistry, Federal University of Lavras, Lavras, Minas Gerais, CEP 37200-000, Brazil.,Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Králové, Czech Republic
| | - Felipe A La Porta
- Laboratório de Nanotecnologia E Química Computacional, Universidade Tecnológica Federal Do Paraná, Londrina, PR, 86036-370, Brazil.
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Lambrinidis G, Tsantili-Kakoulidou A. Multi-objective optimization methods in novel drug design. Expert Opin Drug Discov 2020; 16:647-658. [PMID: 33353441 DOI: 10.1080/17460441.2021.1867095] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Introduction: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that attracts its own research. Current strategies are broadly classified into single - objective optimization (SOO) and multi-objective optimization (MOO).Areas covered: Starting with SOO and the ways used to incorporate multiple criteria into it, the present review focuses on MOO techniques, their comparison, advantages, and restrictions. Pareto analysis and the concept of dominance stand in the core of MOO. The Pareto front, Pareto ranking, and limitations of Pareto-based methods, due to high dimensions and data uncertainty, are outlined. Desirability functions and the weighted sum approaches are described as stand-alone techniques to transform the MOO problem to SOO or in combination with pareto analysis and evolutionary algorithms. Representative applications in different drug research areas are also discussed.Expert opinion: Despite their limitations, the use of combined MOO techniques, as well as being complementary to SOO or in conjunction with artificial intelligence, contributes dramatically to efficient drug design, assisting decisions and increasing success probabilities. For multi-target drug design, optimization is supported by network approaches, while applicability of MOO to other fields like drug technology or biological complexity opens new perspectives in the interrelated fields of medicinal chemistry and molecular biology.
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Affiliation(s)
- George Lambrinidis
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Zografou, Athens, Greece
| | - Anna Tsantili-Kakoulidou
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Zografou, Athens, Greece
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Cerruela-García G, Pérez-Parra Toledano J, de Haro-García A, García-Pedrajas N. Influence of feature rankers in the construction of molecular activity prediction models. J Comput Aided Mol Des 2020; 34:305-325. [PMID: 31893338 DOI: 10.1007/s10822-019-00273-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 12/20/2019] [Indexed: 02/07/2023]
Abstract
In the construction of activity prediction models, the use of feature ranking methods is a useful mechanism for extracting information for ranking features in terms of their significance to develop predictive models. This paper studies the influence of feature rankers in the construction of molecular activity prediction models; for this purpose, a comparative study of fourteen rankings methods for feature selection was conducted. The activity prediction models were constructed using four well-known classifiers and a wide collection of datasets. The ranking algorithms were compared considering the performance of these classifiers using different metrics and the consistency of the ranked features.
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Affiliation(s)
- Gonzalo Cerruela-García
- Department of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 14071, Córdoba, Spain.
| | - José Pérez-Parra Toledano
- Department of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 14071, Córdoba, Spain
| | - Aída de Haro-García
- Department of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 14071, Córdoba, Spain
| | - Nicolás García-Pedrajas
- Department of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 14071, Córdoba, Spain
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