A Computer Conquers Tactical Combinations

Specialists in molecule making often employ the counterintuitive retrosynthetic strategy of initially increasing molecular complexity en route to simpler starting materials to produce synthetic sequences of unparalleled brevity. In this issue of Chem, Gryzbowski and co-workers describe how a computer algorithm can similarly identify these so-called “tactical combinations” and powerfully apply them to automatically identify highly efficient routes to complex molecular targets. Although computers can’t yet outperform masters of synthetic organic chemistry, this breakthrough suggests that this day is coming. Specialists in molecule making often employ the counterintuitive retrosynthetic strategy of initially increasing molecular complexity en route to simpler starting materials to produce synthetic sequences of unparalleled brevity. In this issue of Chem, Gryzbowski and co-workers describe how a computer algorithm can similarly identify these so-called “tactical combinations” and powerfully apply them to automatically identify highly efficient routes to complex molecular targets. Although computers can’t yet outperform masters of synthetic organic chemistry, this breakthrough suggests that this day is coming. For nearly 200 years, synthetic organic chemistry has remained the exclusive domain of highly trained specialists. Other fields have more effectively democratized key elements of their discovery engines—like computer science did with coding and like biology did with genome sequencing—and this has yielded transformative benefits both for these fields and for society. Imagine the impact that could be achieved if molecule making were similarly democratized and the power of the “outsider effect” could finally be harnessed to help drive disruptive innovations on the molecular scale. One of the major barriers to achieving this goal has remained the specialist-dependent nature of molecular synthetic planning. Fortuitously, synthetic planning has also served for more than half a century as a popular testbed for frontier studies in artificial intelligence (AI), and these efforts are starting to bear fruit. In this issue of Chem, an article by Gryzbowski and co-workers represents an important step forward in this rapidly evolving space and has substantial potential to ultimately broaden access to the molecular sandbox.1Gajewska E.P. Szymkuć S. Dittwald P. Popik O. Mlynarski J. Grzybowski B.A. Algorithmic discovery of tactical combinations for advanced organic syntheses.Chem. 2020; 6: 280-293Abstract Full Text Full Text PDF Scopus (24) Google Scholar Strategic planning challenges in general have long been a common testing ground for the development of AI algorithms. Games such as chess (Kasparov versus Deep Blue),2Goodman D. Keene R. Man Versus Machine, Kasparov Versus Deep Blue. H3 Publications, 1997Crossref Google Scholar GO (Sedol versus AlphaGO),3Silver D. Schrittwieser J. Simonyan K. Antonoglou I. Huang A. Guez A. Hubert T. Baker L. Lai M. Bolton A. et al.Mastering the game of GO without human knowledge.Nature. 2017; 550: 354-359Crossref PubMed Scopus (4451) Google Scholar and StarCraft II (Komincz versus AlphaStar)4Vinyals O. Babuschkin I. Czarnecki W.M. Mathieu M. Dudzik A. Chung J. Choi D.H. Powell R. Ewalds T. Georgiev P. et al.Grandmaster level in StarCraft II using multi-agent reinforcement learning.Nature. 2019; 575: 350-354Crossref PubMed Scopus (1140) Google Scholar ultimately yielded landmark achievements where computers exceeded the problem-solving capabilities of humans (Figure 1). The many complex rules, caveats, and subtleties associated with chemical synthesis have to date, however, caused automated retrosynthetic analysis of complex organic molecules to remain a formidable challenge. In a visionary study in 1969, E.J. Corey first approached computer-designed synthetic planning5Corey E.J. Wipke W.T. Computer-assisted design of complex organic syntheses.Science. 1969; 166: 178-192Crossref PubMed Scopus (443) Google Scholar and showed that the naturally occurring perfume agent patchouli alcohol could be disassembled automatically into simple building blocks by a computer according to a set of prescribed rules. Remarkably, this inspiring achievement was made by a computer whose power was less than one-trillionth of the power that is readily available today. Largely enabled by the power of modern computers, there have recently been substantial advances in automated retrosynthesis algorithms.6Filipa de Almeida A. Moreira R. Rodrigues T. Synthetic organic chemistry driven by artificial intelligence.Nat. Rev. Chem. 2019; 3: 589-604Crossref Scopus (117) Google Scholar These include Chematica,7Klucznik T. Mikulak-Klucznik B. McCormack M.P. Lima H. Szymkuc S. Bhowmick M. Molga K. Zhou Y. Rickershauser L. Gajewska E.P. et al.Efficient synthesis of diverse, medicinally relevant targets planned by computer and executed in the laboratory.Chem. 2018; 4: 522-532Abstract Full Text Full Text PDF Scopus (165) Google Scholar similarity searching algorithms,8Coley C.W. Rogers L. Green W.H. Jensen K.F. Computer-assisted retrosynthesis based on molecular similarity.ACS Cent. Sci. 2017; 3: 1237-1245Crossref PubMed Scopus (129) Google Scholar Monte Carlo tree searches coupled to deep neural networks,9Segler M.H.S. Preuss M. Waller M.P. Planning chemical syntheses with deep neural networks and symbolic AI.Nature. 2018; 555: 604-610Crossref PubMed Scopus (812) Google Scholar and gamification of retrosynthesis10Schreck J.S. Coley C.W. Bishop K.J.M. Learning retrosynthetic planning through simulated experience.ACS Cent. Sci. 2019; 5: 970-981Crossref PubMed Scopus (73) Google Scholar (Figure 1). Moderately complex molecules are now readily deconstructed via these approaches, and a commercialized variant of Chematica called Synthia is now available. To date, however, such algorithms have largely remained limited to single-step decision making guided by a requirement for reductions in target complexity in each step. In contrast, experienced practitioners in total synthesis can identify “elegant” sequences that initially increase molecular complexity as a counterintuitive strategy to ultimately streamline a synthetic sequence. Such sequences are referred to as “tactical combinations” (TCs),11Corey E.J. Cheng X.-M. The Logic of Chemical Synthesis. Wiley, 1989Google Scholar and to date, computers have had a hard time discovering and/or utilizing them. The article by Gryzbowski and co-workers is a disruptive step forward in this space.1Gajewska E.P. Szymkuć S. Dittwald P. Popik O. Mlynarski J. Grzybowski B.A. Algorithmic discovery of tactical combinations for advanced organic syntheses.Chem. 2020; 6: 280-293Abstract Full Text Full Text PDF Scopus (24) Google Scholar The authors report the development of an algorithm that analyzes more than a billion possible two-step synthetic sequences to identify a treasure trove of potential TCs, most of which have not yet been reported in the literature. They then incorporate these TCs into the Chematica algorithm, which enables the automated identification of routes that leverage such TCs for the first time. Finally, the authors reduce one of these proposed syntheses to practice making the natural product imperanene with fewer steps than any previously reported synthesis. There remains a gap between the highest-level retrosynthetic planning that can been achieved by specialists and the routes developed by this advanced version of the Chematica algorithm. That said, this paper is exciting because it illuminates a strategy through which this gap could ultimately be closed. Further advances in this direction will require expansion to longer sequences of tactical combinations; however, the scaling of this process could render it too computationally demanding for general usage. There is reason for optimism, however, as computing power continues to expand per Moore’s law, and recent advances in quantum computing suggest that disruptive increases in such power could be on the horizon.12Arute F. Arya K. Babbush R. Bacon D. Bardin J.C. Barends R. Biswas R. Boixo S. Brandao F.G.S.L. Buell D.A. et al.Quantum supremacy using a programmable superconducting processor.Nature. 2019; 574: 505-510Crossref PubMed Scopus (2781) Google Scholar Interfacing such next-generation computerized synthetic-planning algorithms with molecule-making robots13Trobe M. Burke M.D. The molecular industrial revolution: automated synthesis of small molecules.Angew. Chem. Int. Ed. 2018; 57: 4192-4214Crossref PubMed Scopus (113) Google Scholar represents an exciting roadmap for democratizing molecular innovation. The authors gratefully acknowledge the NIH (GM118185) for financial support. D.J.B. is an Illini 4000 postdoctoral fellow of the Damon Runyon Cancer Research Foundation (DRG-2290–17). Algorithmic Discovery of Tactical Combinations for Advanced Organic SynthesesGajewska et al.ChemDecember 19, 2019In BriefTactical combinations (TCs) are two-step reaction sequences that first, in a retrosynthetic direction, complexify the target structure but, by doing so, enable significant structural simplification in subsequent steps. TCs are often hard to identify, and only a few hundred have been cataloged to date. This work shows that computers can now discover TCs having no literature precedent systematically and in large numbers, in effect unlocking new and elegant synthetic approaches. Full-Text PDF Open Access