Electrocatalyzed direct arene alkenylations without directing groups for selective late-stage drug diversification.

Electrooxidation has emerged as an increasingly viable platform in molecular syntheses that can avoid stoichiometric chemical redox agents. Despite major progress in electrochemical C-H activations, these arene functionalizations generally require directing groups to enable the C-H activation. The installation and removal of these directing groups call for additional synthesis steps, which jeopardizes the inherent efficacy of the electrochemical C-H activation approach, leading to undesired waste with reduced step and atom economy. In sharp contrast, herein we present palladium-electrochemical C-H olefinations of simple arenes devoid of exogenous directing groups. The robust electrocatalysis protocol proved amenable to a wide range of both electron-rich and electron-deficient arenes under exceedingly mild reaction conditions, avoiding chemical oxidants. This study points to an interesting approach of two electrochemical transformations for the success of outstanding levels of position-selectivities in direct olefinations of electron-rich anisoles. A physical organic parameter-based machine learning model was developed to predict position-selectivity in electrochemical C-H olefinations. Furthermore, late-stage functionalizations set the stage for the direct C-H olefinations of structurally complex pharmaceutically relevant compounds, thereby avoiding protection and directing group manipulations.

Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge.

Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling of synthetic transformation with the required extrapolative ability and chemical interpretability. To meet the gap between the rich domain knowledge of chemistry and the advanced molecular graph model, herein we report a knowledge-based graph model that embeds the digitalized steric and electronic information. In addition, a molecular interaction module is developed to enable the learning of the synergistic influence of reaction components. In this study, we demonstrate that this knowledge-based graph model achieves excellent predictions of reaction yield and stereoselectivity, whose extrapolative ability is corroborated by additional scaffold-based data splittings and experimental verifications with new catalysts. Because of the embedding of local environment, the model allows the atomic level of interpretation of the steric and electronic influence on the overall synthetic performance, which serves as a useful guide for the molecular engineering towards the target synthetic function. This model offers an extrapolative and interpretable approach for reaction performance prediction, pointing out the importance of chemical knowledge-constrained reaction modelling for synthetic purpose.

Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C-N axial chirality via cobalt catalysis.

Challenging enantio- and diastereoselective cobalt-catalyzed C-H alkylation has been realized by an innovative data-driven knowledge transfer strategy. Harnessing the statistics of a related transformation as the knowledge source, the designed machine learning (ML) model took advantage of delta learning and enabled accurate and extrapolative enantioselectivity predictions. Powered by the knowledge transfer model, the virtual screening of a broad scope of 360 chiral carboxylic acids led to the discovery of a new catalyst featuring an intriguing furyl moiety. Further experiments verified that the predicted chiral carboxylic acid can achieve excellent stereochemical control for the target C-H alkylation, which supported the expedient synthesis for a large library of substituted indoles with C-central and C-N axial chirality. The reported machine learning approach provides a powerful data engine to accelerate the discovery of molecular catalysis by harnessing the hidden value of the available structure-performance statistics.

Exploring Spectrum-based Molecular Descriptors for Reaction Performance Prediction.

Despite the availability and accuracy of modern spectroscopic characterization, the utilization of spectral information in chemical machine learning is still primitive. Here, we report an optical character recognition-based automatic process to utilize spectral information as molecular descriptors, which directly transforms experimental spectrum images to readable vectors. We demonstrate its machine learning application in the reaction yield dataset of Pd-catalyzed Buchwald-Hartwig cross-coupling with aryl halides. In addition, we also show that the predicted spectrum can serve as an alternative encoding source to support the model training.

Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis.

Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential of data-driven prediction of synthesis performance. Digitalization and ML modelling are the key strategies to fully exploit the unique potential within the synergistic interplay between experimental data and the robust prediction of performance and selectivity. A series of exciting studies have demonstrated the importance of chemical knowledge implementation in ML, which improves the model's capability for making predictions that are challenging and often go beyond the abilities of human beings. This Minireview summarizes the cutting-edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022. By merging organic synthesis tactics and chemical informatics, we hope this Review can provide a guide map and intrigue chemists to revisit the digitalization and computerization of organic chemistry principles.

Towards Data-Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning.

Asymmetric hydrogenation of olefins is one of the most powerful asymmetric transformations in molecular synthesis. Although several privileged catalyst scaffolds are available, the catalyst development for asymmetric hydrogenation is still a time- and resource-consuming process due to the lack of predictive catalyst design strategy. Targeting the data-driven design of asymmetric catalysis, we herein report the development of a standardized database that contains the detailed information of over 12000 literature asymmetric hydrogenations of olefins. This database provides a valuable platform for the machine learning applications in asymmetric catalysis. Based on this database, we developed a hierarchical learning approach to achieve predictive machine leaning model using only dozens of enantioselectivity data with the target olefin, which offers a useful solution for the few-shot learning problem and will facilitate the reaction optimization with new olefin substrate in catalysis screening.

Predicting Regioselectivity in Radical C-H Functionalization of Heterocycles through Machine Learning.

Radical C-H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C-H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out-of-sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism-based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.

Abnormal expression of serum soluble E-cadherin is correlated with clinicopathological features and prognosis of breast cancer.

BACKGROUND: Increased amounts of soluble E-cadherin (E-cad) have been found in the serum in various cancers, but the role of serum soluble E-cad in the prognosis of breast cancer patients has not been explored in Asian populations. MATERIAL/METHOD: Blood samples from 111 consecutive patients diagnosed with breast cancer and 55 healthy controls were investigated.Serum soluble E-cad expression levels were measured by enzyme-linked immunosorbent assay(ELISA) with an immunoassay kit according to the manufacturer's directions. Kaplan-Meier analyses were used to evaluate the association between serum soluble E-cad expression level and survival. All statistical tests were 2-sided. RESULTS: The serum levels of soluble E-cad in breast cancer patients were significantly higher than those of the control group (2218.9±319.6 ng/ml vs. 742.8±91.7 ng/ml, p<0.001). Serum levels of soluble E-cad correlated significantly with TNM stage (P=0.007), tumor grade (P=0.03), and lymph node metastasis (P<0.001). Kaplan-Meier analysis with the log-rank test indicated that high serum levels of soluble E-cad had a significant impact on overall survival (55.4% vs. 81.4%; P=0.032) and disease-free survival (36.8% vs. 67.8%; P=0.002) in breast cancer. Multivariate analysis revealed that serum levels of soluble E-cad were independently associated with overall survival and disease-free survival in breast cancer patients. CONCLUSIONS: Serum soluble E-cad level is an independent prognostic factor in Asian breast cancer patients.

Methotrexate selection of long-term culture initiating cells following transduction of CD34(+) cells with a retrovirus containing a mutated human dihydrofolate reductase gene.

A limitation of successful stem cell gene transfer to hematopoietic stem cells is low transduction efficiency. To overcome this hurdle and develop a gene transfer strategy that might be clinically feasible, retroviral vectors containing a drug resistance gene were utilized to transduce human CD34(+)-enriched cells and select gene-modified cells by drug administration. We constructed a high-titer retroviral vector containing a fusion gene (F/S-EGFP) consisting of a mutated dihydrofolate reductase (DHFR) (Leu22-->Phe22, Phe31-->Ser31; F/S) gene and enhanced green fluorescent protein (EGFP) cDNA. To test whether the fusion gene could function as a selectable marker, transduced CD34(+) cells were assayed in long-term stromal co-cultures with and without addition of methotrexate (MTX). Without MTX exposure, the vector-transduced CD34(+) cells generated 22-50% EGFP(+) cobblestone area forming cells (CAFC) at week 5. By contrast, the vector-transduced cells cultured with MTX produced 96-100% EGFP(+) CAFC in four separate experiments. These are the first investigations to demonstrate selection for transduced long-term culture initiating cells using MTX. The DHFR/MTX system holds promise for improving selection of gene-transduced hematopoietic progenitor cells in vivo.