Machine learning (ML) methods reach ever deeper into quantum chemistry and materials simulation, delivering predictive models of interatomic potential energy surfaces1,2,3,4,5,6, molecular forces7,8, electron densities9, density functionals10, and molecular response properties such as polarisabilities11, and infrared spectra12. Large data sets of molecular properties calculated from quantum chemistry or measured from experiment are equally being used to construct predictive models to explore the vast chemical compound space13,14,15,16,17 to find new sustainable catalyst materials18, and to design new synthetic pathways19. Recent research has explored the potential role of machine learning in constructing approximate quantum chemical methods20, as well as predicting MP2 and coupled cluster energies from Hartree–Fock orbitals21,22. There have also been approaches that use neural networks as a basis representation of the wavefunction23,24,25. Most existing ML models have in common that they learn from quantum chemistry to describe molecular properties as scalar, vector, or tensor fields26,27.
Environmentally benign methods for the industrial production of chemicals are urgently needed. LMU researchers recently described such a procedure for the synthesis of formaldehyde, and have now improved it with the aid of machine learning. Formaldehyde is one of the most important feedstocks employed in the chemical industry, and serves as the point of departure for the synthesis of many more complex chemical products. Industrial production of formaldehyde is currently based on a large-scale procedure which consumes fossil fuels and requires a high energy input. More efficient and more sustainable modes of synthesis are therefore urgently needed, which could make a significant contribution to the mitigation of climate.
Agricultural fields are no less than a battlefield. Irrespective of terrain, geography and type, crops have to compete against scores of different weeds, species of hungry insects, nematodes and a broad array of diseases. Weeds, or invasive plants, aggressively compete for soil nutrients, light and water, posing a serious threat to agricultural production and biodiversity. Weeds directly and indirectly result in tremendous losses to the farm sector, which convert to billions each year worldwide. To combat these challenges, the farm sector is looking at Artificial Intelligence (AI) based solutions.
Standing onstage in an ornate conference room at the Delta Bessborough Hotel in downtown Saskatoon, former Saskatchewan premier Dr. Grant Devine pitched the agri-food industry on a new idea: a wheat tube. More specifically, a hypothetical hyperloop Devine says could fire shipments of wheat from Moose Jaw to Langley, B.C. at hundreds of kilometres an hour. He says students at the University of Saskatchewan, where he is a professor, had priced the idea at around $18 billion. "You'd load it like you would any other hopper car, load it in the capsule and -- zoom! -- it's out there in a matter of hours," Devine said. Dr. Grant Devine speaks at the AIC2019 conference in Saskatoon, SK on Wednesday, November 6, 2019.
To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry.
Medopad, the UK startup that has been working with Tencent to develop AI-based methods for building and tracking "digital" biomarkers -- measurable indicators of the progression of illnesses and diseases that are picked up not with blood samples or in-doctor visits but using apps and wearables, has announced another round of funding to expand the scope of its developments. It has picked up $25 million led by pharmaceuticals giant Bayer, which will be working together with Medopad to build digital biomarkers and therapeutics related to heart health. Medopad said it is also working on separate biomarkers related to Parkinson's, Alzheimer's and Diabetes. The Series B is being made at a post-money valuation of between $200 million and $300 million. In addition to Bayer, Hong Kong firm NWS Holdings and Chicago VC Healthbox also participated.
At the annual meeting of the Scientific Advisory Committee on Alternative Toxicological Methods (SACATM; see sidebar), committee members enthusiastically supported advances in new nonanimal testing technologies, such as computational tools and microphysiological systems (MPS), also known as tissue chips. The committee urged regulators to provide clear guidance on how these technologies should be used and what data from them would be accepted. Members also stressed the importance of having high-quality reference data from both human and animal tests to clearly demonstrate the ability of new methods to identify toxic chemicals. Experts from academia, industry, and animal welfare organizations debated how best to use these new technologies in the Sept. 19-20 meeting. The committee meets annually to advise the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM).
When we hear technology we think of electronic gadgets and a hundred types of software. But the problems of the future are going to be more basic. Food, water, and shelter are important to talk about. They're essential to sustain human life and limited in availability. Moreover, the increasing population and concentration of population in major cities will possibly lead to scarcity unless we take due action.
AI in healthcare is having a tremendous impact for the benefit of patients, providers, and payers. The opportunities to deploy AI in health care are increasing exponentially as we become better at capturing and integrating vast amounts of data from multiple sources, making sense of this data in a clinically relevant way, and understand methodologies that explain its use. In some cases, AI will replicate human intelligence, in others it will it will augment what we can do to improve health and lower cost. Pros and cons of various approaches and use cases will be discussed in this exciting session. Recon Strategy is a boutique strategy consulting firm founded in 2010 by alumni of the Boston Consulting Group.
Proteins, the fundamental nanomachines of life, have provided scientists like me with many lessons in our own efforts to create nanomachinery. Proteins are large molecules containing hundreds to thousands of atoms and are typically a few nanometers (billionths of a meter) to tens of nanometers across. Our bodies contain at least 20,000 different proteins that, among other things, cause our muscles to contract, digest our food, build our bones, sense our environment and tirelessly recycle hundreds of small molecules within our cells. As a chemistry undergraduate in 1986, I dreamed of the possibility of designing and synthesizing macromolecules (molecules containing more than 100 atoms) that could do the amazing things that proteins do and more. I have programmed computers since the first TRS-80s came out in the late 1970s, and I thought it would be wonderful if I could build complex molecular machines as easily as I could write software. I wanted to create a programming language for matter--a combination of software and chemistry that would enable people to describe a nanomachines shape and would then determine the series of chemical processes that a chemist or a robot should carry out to build the nanodevice. Unfortunately, the idea of inventing nanomachines by designing new proteins runs into a severe obstacle.