Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory

The Chematica program was used to autonomously design synthetic pathways to eight structurally diverse targets, including seven commercially valuable bioactive substances and one natural product. All of these computer-planned routes were successfully executed in the laboratory and offer significant yield improvements and cost savings over previous approaches, provide alternatives to patented routes, or produce targets that were not synthesized previously. Although computers have demonstrated the ability to challenge humans in various games of strategy, their use in the automated planning of organic syntheses remains unprecedented. As a result of the impact that such a tool could have on the synthetic community, the past half century has seen numerous attempts to create in silico chemical intelligence. However, there has not been a successful demonstration of a synthetic route designed by machine and then executed in the laboratory. Here, we describe an experiment where the software program Chematica designed syntheses leading to eight commercially valuable and/or medicinally relevant targets; in each case tested, Chematica significantly improved on previous approaches or identified efficient routes to targets for which previous synthetic attempts had failed. These results indicate that now and in the future, chemists can finally benefit from having an ???in silico colleague??? that constantly learns, never forgets, and will never retire. Multistep synthetic routes to eight structurally diverse and medicinally relevant targets were planned autonomously by the Chematica computer program, which combines expert chemical knowledge with network-search and artificial-intelligence algorithms. All of the proposed syntheses were successfully executed in the laboratory and offer substantial yield improvements and cost savings over previous approaches or provide the first documented route to a given target. These results provide the long-awaited validation of a computer program in practically relevant synthetic design

Automatic mapping of atoms across both simple and complex chemical reactions

Mapping atoms across chemical reactions is important for substructure searches, automatic extraction of reaction rules, identification of metabolic pathways, and more. Unfortunately, the existing mapping algorithms can deal adequately only with relatively simple reactions but not those in which expert chemists would benefit from computer's help. Here we report how a combination of algorithmics and expert chemical knowledge significantly improves the performance of atom mapping, allowing the machine to deal with even the most mechanistically complex chemical and biochemical transformations. The key feature of our approach is the use of few but judiciously chosen reaction templates that are used to generate plausible "intermediate" atom assignments which then guide a graph-theoretical algorithm towards the chemically correct isomorphic mappings. The algorithm performs significantly better than the available state-of-the-art reaction mappers, suggesting its uses in database curation, mechanism assignments, and - above all - machine extraction of reaction rules underlying modern synthesis-planning programs

Computational design of syntheses leading to compound libraries or isotopically labelled targets

Although computer programs for retrosynthetic planning have shown improved and in some cases quite satisfactory performance in designing routes leading to specific, individual targets, no algorithms capable of planning syntheses of entire target libraries - important in modern drug discovery - have yet been reported. This study describes how network-search routines underlying existing retrosynthetic programs can be adapted and extended to multi-target design operating on one common search graph, benefitting from the use of common intermediates and reducing the overall synthetic cost. Implementation in the Chematica platform illustrates the usefulness of such algorithms in the syntheses of either (i) all members of a user-defined library, or (ii) the most synthetically accessible members of this library. In the latter case, algorithms are also readily adapted to the identification of the most facile syntheses of isotopically labelled targets. These examples are industrially relevant in the context of hitto-lead optimization and syntheses of isotopomers of various bioactive molecules

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest -and hope -that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited -in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors

Looking beyond the hype : applied AI and machine learning in translational medicine

Big data problems are becoming more prevalent for laboratory scientists who look to make clinical impact. A large part of this is due to increased computing power, in parallel with new technologies for high quality data generation. Both new and old techniques of artificial intelligence (AI) and machine learning (ML) can now help increase the success of translational studies in three areas: drug discovery, imaging, and genomic medicine. However, ML technologies do not come without their limitations and shortcomings. Current technical limitations and other limitations including governance, reproducibility, and interpretation will be discussed in this article. Overcoming these limitations will enable ML methods to be more powerful for discovery and reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale

Machine learning for molecular and materials science

Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.</p

Fire resistance of steel-concrete composite columns made out of steel-concrete tubes filled with concrete – available calculation methodologies

Niniejszy artykuł ma na celu przybliżenie zagadnień związanych z obliczaniem nośności słupów zespolonych stalowo-betonowych z rur wypełnionych betonem w warunkach podwyższonej temperatury. Ideą przyświecającą badaniom w opisywanym kierunku jest możliwość projektowania smukłych elementów konstrukcji, które, w odróżnieniu od elementów stalowych, dzięki wykorzystaniu wewnętrznej betonowej części przekroju, nie będą wymagały dodatkowej kosztownej powłoki z farb pęczniejących. Dzięki wykorzystaniu współpracy między stalą a betonem omawiane słupy mogą osiągnąć relatywnie dużą nośność w porównaniu do słupów żelbetowych, przy jednoczesnym zredukowaniu smukłości. W artykule przytoczono dostępne metody obliczeń z norm oraz literatury, a także zestawiono wyniki obliczeń z rezultatami testów laboratoryjnych.The present article is to outline the problem area related to calculation of the load bearing capacity of steel-concrete composite columns made out of concrete-filled tubes, in the context of high temperature conditions exposure. The idea which drives the research, within the field described above, is seen in a possibility of designing slim structural elements which, contrary to the elements made out of steel, thanks to application of concrete within the internal cross section, will not require additional, expensive intumescent coating. Thanks to the fact that a structural relationship emerges between steel and concrete, the columns may achieve relatively high load-bearing capacity, as compared with the ferroconcrete solutions, with simultaneous reduction of their diameter. The present article outlines the available calculation methodology applied within norms and literature. Moreover, results of the calculation has been compared with the laboratory test results

Data for "Numerical investigation of fire and post-fire performance of CFT columns in an open car park fire". SiF Belfast 2018

This dataset contains data for "Numerical investigation of fire and post-fire performance of CFT columns in an open car park fire". Presented at the 10th Structures in Fire conference, Belfast 2018. https://www.ulster.ac.uk/conference/structures-in-fire-2018.If you have any questions when using the provided data, please let me know. I am trying to make my research reproducible, and I will be happy to clear up any problems.This dataset includes: - "SiF18..." folders contain BNDF data obtained with FDS; - "AST GAS matlab code" and "BNDF approach python code" allow replicating the two approaches described in the paper as: "AST GAS approach" and "BNDF approach"; - "Abaqus files" folder includes Abaqus input files (Heat Transfer analysis [HeT] and Mechanical Analysis [HP]) and Fortran subroutine to take into account nonreversible properties of concrete during and after cooling. Questions should be directed to: [email protected] All Abaqus odb files are available upon request, I did not upload them here due to large size. Software used: FDS 6.6.0 and Abaqus 6.12/201

Lightweight cementitious composites containing cenospheres and polypropylene fibres after exposure to high temperatures

The paper presents the results of experimental investigation of lightweight cementitious composites with cenospheres (LCCC) exposed to high temperatures. We showed the positive effect of cenospheres on post- fire residual compressive strength in previous papers. This paper focuses on the LCCC with the addition of polypropylene (PP) fibres. Specimens are heated up to 400, 600, 800, 1000 and 1200 °C. Then they are cooled to ambient temperature and their residual flexural and compressive strength is tested. The results are compared with non-heated specimens with compressive strength above 50 MPa. For plain LCCC composites, the results show significant improvement of residual compressive strength in comparison with typical concretes. No significant changes of compressive strength are found after exposure to temperatures up to 600°C – more than 85 % of the residual compressive strength is retained after exposure to this temperature for both mixes. Polypropylene fibres are found to be a successful mean to mitigate spalling without significantly lowering neither ambient nor residual compressive strength. Moreover, designed composite has low density and low thermal conductivity at room temperature