Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators

Merk, Daniel; Grisoni, Francesca; Friedrich, Lukas; Schneider, Gisbert

doi:10.1038/s42004-018-0068-1

Cited by 107 publications

(120 citation statements)

References 33 publications

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“…For computational de novo design of cell migration inducers we employed a pre‐trained RNN model that had successfully been used for generating bioactive new chemical entities in previous studies . This model was fine‐tuned by transfer‐learning using a collection of compounds exhibiting CXCR4 antagonistic activity in a variety of in vitro test systems with a potency threshold of <1 μM (“template” collection).…”

Section: Resultsmentioning

confidence: 99%

“…We previously applied an RNN with long short‐term memory cells (LSTM), which was trained with bioactive small molecules from ChEMBL, and by fine‐tuning with small sets of known bioactives (transfer learning), reoriented to generate target‐specific compounds. Prospective applications demonstrated that nuclear receptor modulators and natural‐product‐inspired bioactive molecules which were synthetically accessible and confirmed as having in vitro activity against the desired targets, could be obtained using this approach . Here, we apply this constructive machine learning approach to the computational design of cell migration modulators.…”

Section: Introductionmentioning

confidence: 99%

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Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

et al. 2019

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Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell‐migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top‐scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

et al. 2019

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“…Merk, Schneider and coworkers validated the SMILES-LSTM transfer learning method 14 by generating compounds 1-3 for nuclear receptors, which after synthesis and testing showed micro-to nanomolar activity (see Figure 1). 66 Figure 1: Compounds 1-3, generated by a SMILES-LSTM model, 14 were made and tested, and showed micro-to nanomolar activity against the RXR and PPAR receptors. 66,67 An important result about the space that pretrained neural models can explore was recently published by Arus-Pous et al 68 They studied whether SMILES RNNs trained on a small subset (0.1%) of GDB13, an enumerated molecule set, which covers all potentially stable molecules up to 13 heavy atoms, could recover almost the full dataset after sampling a sufficient number of compounds.…”

Section: Models For De Novo Molecular Designmentioning

confidence: 99%

GuacaMol: Benchmarking Models for de Novo Molecular Design

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Segler

et al. 2019

J. Chem. Inf. Model.

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De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural networks appeared recently and show promising results. However, the new models have not been profiled on consistent tasks, and comparative studies to well-established algorithms have only seldom been performed. To standardize the assessment of both classical and neural models for de novo molecular design, we propose an evaluation framework, GuacaMol, based on a suite of standardized benchmarks. The benchmark tasks encompass measuring the fidelity of the models to reproduce the property distribution of the training sets, the ability to generate novel molecules, the exploration and exploitation of chemical space, and a variety of single and multi-objective optimization tasks. The benchmarking open-source Python code, and a leaderboard can be found on https://benevolent.ai/guacamol.

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“…The process continues with the screening of the available databases for finding molecules that possess similar fingerprints to the generated ones. Generative models and deep neural networks (DLNs) have thus allowed generating molecules and promising candidates for useful drugs, basically from scratch, making it possible to "design perfect needles instead of searching for a needle in a haystack" (White and Wilson, 2010;Benjamin et al, 2017;Gómez-Bombarelli et al, 2018;Harel and Radinsky, 2018;Kang and Cho, 2018;Li et al, 2018b;Merk et al, 2018;Nouira et al, 2018;Popova et al, 2018;Sanchez-Lengeling and Aspuru-Guzik, 2018;Schneider, 2018).…”

Section: Co-occurring Machine-learning Contributions In Chemical Sciementioning

confidence: 99%

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

2019

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Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.

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Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators

Cited by 107 publications

References 33 publications

Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

GuacaMol: Benchmarking Models for de Novo Molecular Design

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

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