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  1. AU="Noutahi, Emmanuel"
  2. AU="Renault, Lucie"
  3. AU="Verduzco-Gutierrez, Monica"
  4. AU="Lavelle, Olivier"
  5. AU="Wan, Y I"
  6. AU="Ouellette-Frève, Johann-François"
  7. AU="Vakirlis, Nikolaos"
  8. AU="Petukhova, Lynn"
  9. AU="Lorenzo-López, Laura"
  10. AU="Méndez, Lucía I"
  11. AU="Gaißer, M"
  12. AU="Spengler, Andrea"
  13. AU="Patel, Dilipkumar"
  14. AU="Liu, Wenshuo"
  15. AU="Paldusová, B."
  16. AU="Kemp, Joanna"
  17. AU="Wang, J. Y"
  18. AU="Reddy, Vineet N"
  19. AU="Htway, Zin"
  20. AU=Higashihara Kazuya
  21. AU="Bhat, Swapna"
  22. AU="Christofer Lundqvist"
  23. AU="Mendez, Carol"
  24. AU="Cosimi, Anthony Benedict"
  25. AU="Rhoton, Albert L"
  26. AU="Ahmad, Ahmir"
  27. AU="Salewski, D L"
  28. AU="Akaichi, Faical"
  29. AU="Terry, C C"
  30. AU="VanMorlan, Amie M"
  31. AU="Marcus, Gail"

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  1. Artikel ; Online: Real-World Molecular Out-Of-Distribution: Specification and Investigation.

    Tossou, Prudencio / Wognum, Cas / Craig, Michael / Mary, Hadrien / Noutahi, Emmanuel

    Journal of chemical information and modeling

    2024  Band 64, Heft 3, Seite(n) 697–711

    Abstract: This study presents a rigorous framework for investigating molecular out-of-distribution (MOOD) generalization in drug discovery. The concept of MOOD is first clarified through ... ...

    Abstract This study presents a rigorous framework for investigating molecular out-of-distribution (MOOD) generalization in drug discovery. The concept of MOOD is first clarified through a
    Sprache Englisch
    Erscheinungsdatum 2024-02-01
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.3c01774
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning.

    Horwood, Julien / Noutahi, Emmanuel

    ACS omega

    2020  Band 5, Heft 51, Seite(n) 32984–32994

    Abstract: The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria. Despite recent progress, we argue that existing generative methods are limited in their ability to ... ...

    Abstract The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria. Despite recent progress, we argue that existing generative methods are limited in their ability to favorably shift the distributions of molecular properties during optimization. We instead propose a novel Reinforcement Learning framework for molecular design in which an agent learns to directly optimize through a space of synthetically accessible drug-like molecules. This becomes possible by defining transitions in our Markov decision process as chemical reactions and allows us to leverage synthetic routes as an inductive bias. We validate our method by demonstrating that it outperforms existing state-of-the-art approaches in the optimization of pharmacologically relevant objectives, while results on multi-objective optimization tasks suggest increased scalability to realistic pharmaceutical design problems.
    Sprache Englisch
    Erscheinungsdatum 2020-12-15
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 2470-1343
    ISSN (online) 2470-1343
    DOI 10.1021/acsomega.0c04153
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Buch ; Online: Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning

    Horwood, Julien / Noutahi, Emmanuel

    2020  

    Abstract: The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria. Despite recent progress, we argue that existing generative methods are limited in their ability to ... ...

    Abstract The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria. Despite recent progress, we argue that existing generative methods are limited in their ability to favourably shift the distributions of molecular properties during optimization. We instead propose a novel Reinforcement Learning framework for molecular design in which an agent learns to directly optimize through a space of synthetically-accessible drug-like molecules. This becomes possible by defining transitions in our Markov Decision Process as chemical reactions, and allows us to leverage synthetic routes as an inductive bias. We validate our method by demonstrating that it outperforms existing state-of the art approaches in the optimization of pharmacologically-relevant objectives, while results on multi-objective optimization tasks suggest increased scalability to realistic pharmaceutical design problems.
    Schlagwörter Physics - Chemical Physics ; Computer Science - Machine Learning ; Statistics - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2020-04-29
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Artikel ; Online: GATC: a genetic algorithm for gene tree construction under the Duplication-Transfer-Loss model of evolution.

    Noutahi, Emmanuel / El-Mabrouk, Nadia

    BMC genomics

    2018  Band 19, Heft Suppl 2, Seite(n) 102

    Abstract: Background: Several methods have been developed for the accurate reconstruction of gene trees. Some of them use reconciliation with a species tree to correct, a posteriori, errors in gene trees inferred from multiple sequence alignments. Unfortunately ... ...

    Abstract Background: Several methods have been developed for the accurate reconstruction of gene trees. Some of them use reconciliation with a species tree to correct, a posteriori, errors in gene trees inferred from multiple sequence alignments. Unfortunately the best fit to sequence information can be lost during this process.
    Results: We describe GATC, a new algorithm for reconstructing a binary gene tree with branch length. GATC returns optimal solutions according to a measure combining both tree likelihood (according to sequence evolution) and a reconciliation score under the Duplication-Transfer-Loss (DTL) model. It can either be used to construct a gene tree from scratch or to correct trees infered by existing reconstruction method, making it highly flexible to various input data types. The method is based on a genetic algorithm acting on a population of trees at each step. It substantially increases the efficiency of the phylogeny space exploration, reducing the risk of falling into local minima, at a reasonable computational time. We have applied GATC to a dataset of simulated cyanobacterial phylogenies, as well as to an empirical dataset of three reference gene families, and showed that it is able to improve gene tree reconstructions compared with current state-of-the-art algorithms.
    Conclusion: The proposed algorithm is able to accurately reconstruct gene trees and is highly suitable for the construction of reference trees. Our results also highlight the efficiency of multi-objective optimization algorithms for the gene tree reconstruction problem. GATC is available on Github at: https://github.com/UdeM-LBIT/GATC .
    Mesh-Begriff(e) Algorithms ; Cyanobacteria/genetics ; Evolution, Molecular ; Gene Duplication ; Genes, Bacterial ; Genomics/methods ; Internet ; Models, Genetic ; Multigene Family ; Phylogeny
    Sprache Englisch
    Erscheinungsdatum 2018-05-09
    Erscheinungsland England
    Dokumenttyp Journal Article
    ISSN 1471-2164
    ISSN (online) 1471-2164
    DOI 10.1186/s12864-018-4455-x
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Buch ; Online: Role of Structural and Conformational Diversity for Machine Learning Potentials

    Shenoy, Nikhil / Tossou, Prudencio / Noutahi, Emmanuel / Mary, Hadrien / Beaini, Dominique / Ding, Jiarui

    2023  

    Abstract: In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically conformational and structural diversity, and model generalization is critical in improving the quality of Quantum ... ...

    Abstract In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically conformational and structural diversity, and model generalization is critical in improving the quality of Quantum Mechanics (QM) data generation efforts. We investigate these dynamics through two distinct experiments: a fixed budget one, where the dataset size remains constant, and a fixed molecular set one, which focuses on fixed structural diversity while varying conformational diversity. Our results reveal nuanced patterns in generalization metrics. Notably, for optimal structural and conformational generalization, a careful balance between structural and conformational diversity is required, but existing QM datasets do not meet that trade-off. Additionally, our results highlight the limitation of the MLIP models at generalizing beyond their training distribution, emphasizing the importance of defining applicability domain during model deployment. These findings provide valuable insights and guidelines for QM data generation efforts.

    Comment: Accepted at NeurIPS 2023 AI4D3 and AI4S workshops
    Schlagwörter Physics - Chemical Physics ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-10-30
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  6. Buch ; Online: Gotta be SAFE

    Noutahi, Emmanuel / Gabellini, Cristian / Craig, Michael / Lim, Jonathan S. C / Tossou, Prudencio

    A New Framework for Molecular Design

    2023  

    Abstract: Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of molecular substructures. To address this issue, we introduce Sequential Attachment-based Fragment ... ...

    Abstract Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of molecular substructures. To address this issue, we introduce Sequential Attachment-based Fragment Embedding (SAFE), a novel line notation for chemical structures. SAFE reimagines SMILES strings as an unordered sequence of interconnected fragment blocks while maintaining compatibility with existing SMILES parsers. It streamlines complex generative tasks, including scaffold decoration, fragment linking, polymer generation, and scaffold hopping, while facilitating autoregressive generation for fragment-constrained design, thereby eliminating the need for intricate decoding or graph-based models. We demonstrate the effectiveness of SAFE by training an 87-million-parameter GPT2-like model on a dataset containing 1.1 billion SAFE representations. Through targeted experimentation, we show that our SAFE-GPT model exhibits versatile and robust optimization performance. SAFE opens up new avenues for the rapid exploration of chemical space under various constraints, promising breakthroughs in AI-driven molecular design.

    Comment: Code, data and models available at: https://github.com/datamol-io/safe/
    Schlagwörter Computer Science - Machine Learning ; Quantitative Biology - Biomolecules
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-10-16
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  7. Artikel ; Online: Rapid Genetic Code Evolution in Green Algal Mitochondrial Genomes.

    Noutahi, Emmanuel / Calderon, Virginie / Blanchette, Mathieu / El-Mabrouk, Nadia / Lang, Bernd Franz

    Molecular biology and evolution

    2019  Band 36, Heft 4, Seite(n) 766–783

    Abstract: Genetic code deviations involving stop codons have been previously reported in mitochondrial genomes of several green plants (Viridiplantae), most notably chlorophyte algae (Chlorophyta). However, as changes in codon recognition from one amino acid to ... ...

    Abstract Genetic code deviations involving stop codons have been previously reported in mitochondrial genomes of several green plants (Viridiplantae), most notably chlorophyte algae (Chlorophyta). However, as changes in codon recognition from one amino acid to another are more difficult to infer, such changes might have gone unnoticed in particular lineages with high evolutionary rates that are otherwise prone to codon reassignments. To gain further insight into the evolution of the mitochondrial genetic code in green plants, we have conducted an in-depth study across mtDNAs from 51 green plants (32 chlorophytes and 19 streptophytes). Besides confirming known stop-to-sense reassignments, our study documents the first cases of sense-to-sense codon reassignments in Chlorophyta mtDNAs. In several Sphaeropleales, we report the decoding of AGG codons (normally arginine) as alanine, by tRNA(CCU) of various origins that carry the recognition signature for alanine tRNA synthetase. In Chromochloris, we identify tRNA variants decoding AGG as methionine and the synonymous codon CGG as leucine. Finally, we find strong evidence supporting the decoding of AUA codons (normally isoleucine) as methionine in Pycnococcus. Our results rely on a recently developed conceptual framework (CoreTracker) that predicts codon reassignments based on the disparity between DNA sequence (codons) and the derived protein sequence. These predictions are then validated by an evaluation of tRNA phylogeny, to identify the evolution of new tRNAs via gene duplication and loss, and structural modifications that lead to the assignment of new tRNA identities and a change in the genetic code.
    Mesh-Begriff(e) Chlorophyta/genetics ; Evolution, Molecular ; Genetic Code ; Genome, Mitochondrial ; Phylogeny ; RNA, Transfer/genetics
    Chemische Substanzen RNA, Transfer (9014-25-9)
    Sprache Englisch
    Erscheinungsdatum 2019-01-31
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 998579-7
    ISSN 1537-1719 ; 0737-4038
    ISSN (online) 1537-1719
    ISSN 0737-4038
    DOI 10.1093/molbev/msz016
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Buch ; Online: Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling

    Noutahi, Emmanuel / Beaini, Dominique / Horwood, Julien / Giguère, Sébastien / Tossou, Prudencio

    2019  

    Abstract: Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks. Unfortunately, GNNs often fail to capture the relative importance of interactions between molecular substructures, in part due to the ...

    Abstract Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks. Unfortunately, GNNs often fail to capture the relative importance of interactions between molecular substructures, in part due to the absence of efficient intermediate pooling steps. To address these issues, we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable hierarchical graph pooling method that takes into account both node features and graph structure to improve molecular representation. We benchmark LaPool on molecular graph prediction and understanding tasks and show that it outperforms recent GNNs. Interestingly, LaPool also remains competitive on non-molecular tasks. Both quantitative and qualitative assessments are done to demonstrate LaPool's improved interpretability and highlight its potential benefits in drug design. Finally, we demonstrate LaPool's utility for the generation of valid and novel molecules by incorporating it into an adversarial autoencoder.

    Comment: 11 pages, with Appendices
    Schlagwörter Computer Science - Machine Learning ; Quantitative Biology - Biomolecules ; Statistics - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2019-05-27
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  9. Artikel ; Online: Three-dimensional structure model and predicted ATP interaction rewiring of a deviant RNA ligase 2.

    Moreira, Sandrine / Noutahi, Emmanuel / Lamoureux, Guillaume / Burger, Gertraud

    BMC structural biology

    2015  Band 15, Seite(n) 20

    Abstract: Background: RNA ligases 2 are scarce and scattered across the tree of life. Two members of this family are well studied: the mitochondrial RNA editing ligase from the parasitic trypanosomes (Kinetoplastea), a promising drug target, and bacteriophage T4 ... ...

    Abstract Background: RNA ligases 2 are scarce and scattered across the tree of life. Two members of this family are well studied: the mitochondrial RNA editing ligase from the parasitic trypanosomes (Kinetoplastea), a promising drug target, and bacteriophage T4 RNA ligase 2, a workhorse in molecular biology. Here we report the identification of a divergent RNA ligase 2 (DpRNL) from Diplonema papillatum (Diplonemea), a member of the kinetoplastids' sister group.
    Methods: We identified DpRNL with methods based on sensitive hidden Markov Model. Then, using homology modeling and molecular dynamics simulations, we established a three dimensional structure model of DpRNL complexed with ATP and Mg2+.
    Results: The 3D model of Diplonema was compared with available crystal structures from Trypanosoma brucei, bacteriophage T4, and two archaeans. Interaction of DpRNL with ATP is predicted to involve double π-stacking, which has not been reported before in RNA ligases. This particular contact would shift the orientation of ATP and have considerable consequences on the interaction network of amino acids in the catalytic pocket. We postulate that certain canonical amino acids assume different functional roles in DpRNL compared to structurally homologous residues in other RNA ligases 2, a reassignment indicative of constructive neutral evolution. Finally, both structure comparison and phylogenetic analysis show that DpRNL is not specifically related to RNA ligases from trypanosomes, suggesting a unique adaptation of the latter for RNA editing, after the split of diplonemids and kinetoplastids.
    Conclusion: Homology modeling and molecular dynamics simulations strongly suggest that DpRNL is an RNA ligase 2. The predicted innovative reshaping of DpRNL's catalytic pocket is worthwhile to be tested experimentally.
    Mesh-Begriff(e) Adenosine Triphosphate/metabolism ; Catalytic Domain ; Euglenozoa/chemistry ; Euglenozoa/enzymology ; Euglenozoa/genetics ; Magnesium/metabolism ; Markov Chains ; Models, Molecular ; Molecular Docking Simulation ; Molecular Dynamics Simulation ; Phylogeny ; Protozoan Proteins/chemistry ; Protozoan Proteins/genetics ; Protozoan Proteins/metabolism ; RNA Ligase (ATP)/chemistry ; RNA Ligase (ATP)/genetics ; RNA Ligase (ATP)/metabolism ; Structural Homology, Protein
    Chemische Substanzen Protozoan Proteins ; Adenosine Triphosphate (8L70Q75FXE) ; RNA Ligase (ATP) (EC 6.5.1.3) ; Magnesium (I38ZP9992A)
    Sprache Englisch
    Erscheinungsdatum 2015-10-09
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2050440-8
    ISSN 1472-6807 ; 1472-6807
    ISSN (online) 1472-6807
    ISSN 1472-6807
    DOI 10.1186/s12900-015-0046-0
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  10. Artikel: Evolution through segmental duplications and losses: a Super-Reconciliation approach.

    Delabre, Mattéo / El-Mabrouk, Nadia / Huber, Katharina T / Lafond, Manuel / Moulton, Vincent / Noutahi, Emmanuel / Castellanos, Miguel Sautie

    Algorithms for molecular biology : AMB

    2020  Band 15, Seite(n) 12

    Abstract: The classical gene and species tree reconciliation, used to infer the history of gene gain and loss explaining the evolution of gene families, assumes an independent evolution for each family. While this assumption is reasonable for genes that are far ... ...

    Abstract The classical gene and species tree reconciliation, used to infer the history of gene gain and loss explaining the evolution of gene families, assumes an independent evolution for each family. While this assumption is reasonable for genes that are far apart in the genome, it is not appropriate for genes grouped into syntenic blocks, which are more plausibly the result of a concerted evolution. Here, we introduce the
    Sprache Englisch
    Erscheinungsdatum 2020-05-26
    Erscheinungsland England
    Dokumenttyp Journal Article
    ISSN 1748-7188
    ISSN 1748-7188
    DOI 10.1186/s13015-020-00171-4
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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