AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge

Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they utilize their experiments, intuition, and knowledge. Recent breakthroughs in machine learning (ML) techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human intervention. However, learning chemical knowledge by ML for practical synthesis planning has not yet been adequately achieved and remains a challenging problem. In this study, we developed a data-driven CASP application integrated with various portions of retrosynthesis knowledge called “ReTReK” that introduces the knowledge as adjustable parameters into the evaluation of promising search directions. The experimental results showed that ReTReK successfully searched synthetic routes based on the specified retrosynthesis knowledge, indicating that the synthetic routes searched with the knowledge were preferred to those without the knowledge. The concept of integrating retrosynthesis knowledge as adjustable parameters into a data-driven CASP application is expected to enhance the performance of both existing data-driven CASP applications and those under development

kGCN: a graph-based deep learning framework for chemical structures

Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multi-modal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo

Development of thermodynamic and kinetic databases in micro-soldering alloy systems and their applications

AbstractRecent progress in the development of thermodynamic and kinetic databases of micro-soldering alloys, which were constructed within the framework of the Thermo-Calc and DICTRA software, was presented. Especially, a thermodynamic tool, ADAMIS (alloy database for micro-solders) was developed by combining the thermodynamic databases of micro-solders with Pandat, a multi-component phase diagram calculation software program. ADAMIS contains 11 elements, namely, Ag, Al, Au, Bi, Cu, In, Ni, Sb, Sn, Zn and Pb, and can handle all combinations of these elements in the whole composition range. The obtained thermodynamic and kinetic databases can not only provide much valuable thermodynamic information such as phase equilibria and phase fraction, but also shows the kinetics and the evolution of microstructures when they are combined with some appropriate software programs and models, such as the phase field method and ADSTEFAN software. From the viewpoints of computational thermodynamics and kinetics, some technical examples were given to demonstrate the great utility of these databases for the applications in the development of micro-soldering materials. These databases are expected to be powerful tools for the development of micro-solders and Cu substrate materials, as well as for promoting the understanding of interfacial phenomena and microstructure evolution between solders and substrates in electronic packaging technology