Mapping the Space of Chemical Reactions Using Attention-Based Neural Networks

Organic reactions are usually assigned to classes containing reactions with similar reagents and mechanisms. Reaction classes facilitate the communication of complex concepts and efficient navigation through chemical reaction space. However, the classification process is a tedious task. It requires the identification of the corresponding reaction class template via annotation of the number of molecules in the reactions, the reaction center, and the distinction between reactants and reagents. This work shows that transformer-based models can infer reaction classes from non-annotated, simple text-based representations of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We also show that the learned representations can be used as reaction fingerprints that capture fine-grained differences between reaction classes better than traditional reaction fingerprints. The insights into chemical reaction space enabled by our learned fingerprints are illustrated by an interactive reaction atlas providing visual clustering and similarity searching.Comment: https://rxn4chemistry.github.io/rxnfp

Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search.

Computer-aided synthesis planning (CASP) aims to automatically learn organic reactivity from literature and perform retrosynthesis of unseen molecules. CASP systems must learn reactions sufficiently precisely to propose realistic disconnections, while avoiding overfitting to leave room for diverse options, and explore possible routes such as to allow short synthetic sequences to emerge. Herein we report an open-source CASP tool proposing original solutions to both challenges. First, we use a triple transformer loop (TTL) predicting starting materials (T1), reagents (T2), and products (T3) to explore various disconnection sites defined by combining systematic, template-based, and transformer-based tagging procedures. Second, we integrate TTL into a multistep tree search algorithm (TTLA) prioritizing sequences using a route penalty score (RPScore) considering the number of steps, their confidence score, and the simplicity of all intermediates along the route. Our approach favours short synthetic routes to commercial starting materials, as exemplified by retrosynthetic analyses of recently approved drugs

Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search

Computer-aided synthesis planning (CASP) aims to automatically learn organic reactivity from literature and perform retrosynthesis of unseen molecules. CASP systems must learn reactions sufficiently precisely to propose realistic disconnections while avoiding overfitting to leave room for diverse options, and explore possible routes such as to allow short synthetic sequences to emerge. Herein we report an open-source CASP tool proposing original solutions to both challenges. First, we use a triple transformer loop (TTL) predicting starting materials (T1), reagents (T2), and products (T3) to explore various disconnections sites defined by combining exhaustive, template-based and transformer-based tagging procedures. Second, we integrate TTL into a multistep tree search algorithm (TTLA) prioritizing sequences using a route penalty score (RPScore) considering the number of steps, their confidence score, and the simplicity of all intermediates along the route. Our approach favours short synthetic routes to commercial starting materials, as exemplified by retrosynthetic analyses of recently approved drugs

Predicting Biotransformations with a Molecular Transformer

The use of enzymes for organic synthesis allows for simplified, more economical and selective synthetic routes not accessible to conventional reagents. However, predicting whether a particular molecule might undergo a specific enzyme transformation is very difficult. Here we exploited recent advances in computer assisted synthetic planning (CASP) by considering the molecular transformer, which is a sequence-to-sequence machine learning model that can be trained to predict the products of organic transformations, including their stereochemistry, from the structure of reactants and reagents. We used multi-task transfer learning to train the molecular transformer with one million reactions from the US Patent Office (USPTO) database as a source of general chemistry knowledge combined with 32,000 enzymatic transformations, each one annotated with a text description of the enzyme. We show that the resulting Enzymatic Transformer model predicts the products formed from a given substrate and enzyme with remarkable accuracy, including typical kinetic resolution processes

Mapping the Space of Chemical Reactions using Attention-Based Neural Networks

Organic reactions are usually assigned to classes grouping reactions with similar reagents and mechanisms. Reaction classes facilitate communication of complex concepts and efficient navigation through chemical reaction space. However, the classification process is a tedious task, requiring the identification of the corresponding reaction class template via annotation of the number of molecules in the reactions, the reaction center and the distinction between reactants and reagents. In this work, we show that transformer-based models can infer reaction classes from non-annotated, simple text-based representations of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We also show that the learned representations can be used as reaction fingerprints which capture fine-grained differences between reaction classes better than traditional reaction fingerprints. The unprecedented insights into chemical reaction space enabled by our learned fingerprints is illustrated by an interactive reaction atlas providing visual clustering and similarity searching. Code: https://github.com/rxn4chemistry/rxnfpTutorials: https://rxn4chemistry.github.io/rxnfp/Interactive reaction atlas: https://rxn4chemistry.github.io/rxnfp//tmaps/tmap_ft_10k.html</div

Mapping the space of chemical reactions using attention-based neural networks

Organic reactions are usually assigned to classes containing reactions with similar reagents and mechanisms. Reaction classes facilitate the communication of complex concepts and efficient navigation through chemical reaction space. However, the classification process is a tedious task. It requires identification of the corresponding reaction class template via annotation of the number of molecules in the reactions, the reaction centre and the distinction between reactants and reagents. Here, we show that transformer-based models can infer reaction classes from non-annotated, simple text-based representations of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We also show that the learned representations can be used as reaction fingerprints that capture fine-grained differences between reaction classes better than traditional reaction fingerprints. The insights into chemical reaction space enabled by our learned fingerprints are illustrated by an interactive reaction atlas providing visual clustering and similarity searching

O-(2-18F-fluoroethyl)-l-tyrosine (18F-FET) uptake in insulinoma: first results from a xenograft mouse model and from human.

Herein we have evaluated the uptake of O-(2-DynamicSeven and three nude mice bearing a RIN-m5F insulinoma xenograft were respectively studied byF-FET PET compared equally tojournal article2017 Oct2017 07 12importe

Effect of Carbidopa on 18 F-FDOPA Uptake in Insulinoma: From Cell Culture to Small-Animal PET Imaging

International audiencePatient premedication with carbidopa seems to improve the accuracy of 6-F-18-fluoro-3,4-dihydroxy-L-phenylalanine (F-18-FDOPA) PET for insulinoma diagnosis. However, the risk of PET false-negative results in the presence of carbidopa is a concern. Consequently, we aimed to evaluate the effect of carbidopa on F-18-FDOPA uptake in insulinoma beta-cells and an insulinoma xenograft model in mice. Methods: F-18-FDOPA in vitro accumulation was assessed in the murine beta-cell line RIN-m5F. In vivo small-animal PET experiments were performed on tumor-bearing nude mice after subcutaneous injection of RIN-m5F cells. Experiments were conducted with and without carbidopa pretreatment. Results: Incubation of RIN-m5F cells with 80 mu M carbidopa did not significantly affect the cellular accumulation of F-18-FDOPA. Tumor xenografts were clearly detectable by small-animal PET in all cases. Insulinoma xenografts in carbidopa-treated mice showed significantly higher F-18-FDOPA uptake than those in nontreated mice. Regardless of carbidopa premedication, the xenografts were characterized by an early increase in F-18-FDOPA uptake and then a progressive reduction over time. Conclusion: Carbidopa did not influence in vitro F-18-FDOPA accumulation in RIN-m5F cells but improved insulinoma imaging in vivo. Our findings increase current knowledge about the F-18-FDOPA uptake profile of RIN-m5F cells and a related xenograft model. To our knowledge, the present work represents the first pre clinical research specifically focused on insulinomas, with potential translational implications

The selectivity and structural determinants of peptide antagonists at the CGRP receptor of rat, L6 myocytes

1. Potency orders were determined for a series of agonists and antagonists on the calcitonin gene-related peptide (CGRP) receptor of rat L6 myocytes. The agents tested were all shown to have been active against CGRP, amylin or adrenomedullin receptors. 2. AC187 had a pIC(50) of 6.8±0.10, making it 14 fold less potent as an antagonist than CGRP(8–37) (pIC(50), 7.95±0.14). Amyline(8–37) was equipotent to AC187 (pIC(50), 6.6±0.16) and CGRP(19–37) was 3 fold less potent than either (pIC(50), 6.1±0.24). 3. [Ala(11)]-CGRP(8–37) was 6 fold less potent than CGRP(8–37), (pIC(50), 7.13±0.14), whereas [Ala(18)]-CGRP(8–37) was approximately equipotent to CGRP(8–37) (pIC(50), 7.52±0.15). However, [Ala(11),Ala(18)]-CGRP(8–37) was over 300 fold less potent than CGRP(8–37) (pIC(50), 5.30±0.04). 4. [Tyr(0)]-CGRP(28–37), amylin(19–37) and adrenomedullin(22–52) were inactive as antagonists at concentrations of up to 1 μM. 5. Biotinyl-human α-CGRP was 150 fold less potent than human α-CGRP itself (EC(50) values of 48±17 nM and 0.31±0.13 nM, respectively). At 1 μM, [Cys(acetomethoxy)(2,7)]-CGRP was inactive as an agonist. 6. These results confirm a role for Arg(11) in maintaining the high affinity binding of CGRP(8–37). Arg(18) is of less direct significance for high affinity binding, but it may be important in maintaining the amphipathic nature of CGRP and its analogues

Amylin activates glycogen phosphorylase in the isolated soleus muscle of the rat

AbstractThe pancreatic β-cell hormone amylin acts in isolated rat skeletal muscle to decrease insulin-stimulated incorporation of glucose into glycogen. It also increases blood levels of lactate and glucose in fasted rats in vivo. However, it remained uncertain whether amylin exerts direct effects to stimulate muscle glycogenolysis. We now report that amylin caused a dose-dependent increase in activity of muscle glycogen phosphorylase in isolated rat soleus muscle by stimulating phosphorylase a. Insulin inhibited amylin-stimulated activation of phosphorylase. Effects of amylin to stimulate muscle glycogenolysis are consistent with observed effects of amylin in vivo and could be a major mechanism whereby amylin modulates carbohydrate metabolism