Reviews ChemPhysChem doi.org/10.1002/cphc.202000518 1 2 3 Chemistry in Times of Artificial Intelligence 4 [a] 5 Johann Gasteiger* 6 7 Dedicated to the memory of Professor Rolf Huisgen who passed away on March 26, 2020, shortly before his 100th birthday. 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 ChemPhysChem 2020, 21, 2233– 2242 2233 © 2020 The Authors. Published by Wiley-VCH GmbH Wiley VCH Montag, 05.10.2020 2020 / 179989 [S. 2233/2242] 1 Reviews ChemPhysChem doi.org/10.1002/cphc.202000518 1 Chemists have to a large extent gained their knowledge by the elucidation of the structure of molecules. This eventually 2 doing experiments and thus gather data. By putting various led to a discipline of its own: chemoinformatics. Chemo- 3 data together and then analyzing them, chemists have fostered informatics has found important applications in the fields of 4 their understanding of chemistry. Since the 1960s, computer drug discovery, analytical chemistry, organic chemistry, agri- 5 methods have been developed to perform this process from chemical research, food science, regulatory science, material 6 data to information to knowledge. Simultaneously, methods science, and process control. From its inception, chemoinfor- 7 were developed for assisting chemists in solving their funda- matics has utilized methods from artificial intelligence, an 8 mental questions such as the prediction of chemical, physical, approach that has recently gained more momentum. 9 or biological properties, the design of organic syntheses, and 10 11 1. Introduction accelerate chemical innovation. It will further be shown where 12 new methods from artificial intelligence are introduced into 13 Artificial Intelligence (AI) has entered many domains of society, various fields of chemistry to further assist in understanding 14 and artificial intelligence methods are used for such diverse chemical data. 15 tasks as human speech recognition, successfully competing 16 with experts in strategic games (like chess and GO), and 17 autonomously operating cars. These methods essentially derive 2. Learning in Chemistry 18 their power by learning from data and are sometimes called 19 machine learning or even deep learning. Fortunately, concomitant with this vast increase in chemical 20 Chemistry has from the very beginning derived its knowl- data, computer technology arrived and rapidly became more 21 edge from data. Chemists have run experiments to obtain data and more powerful. Thus, computers could be used to make 22 on chemical or physical properties, on chemical reactions, or on mathematical operations solving equations such as those 23 biological activities. These data were then used to make encountered in quantum mechanics (QM), the theory that 24 predictions by analogy or to derive models for the principles underlies chemistry. This allowed the calculation of physical 25 that underly the data. To foster an understanding of chemistry and chemical data by QM methods of increasing complexity. 26 in students Rolf Huisgen has written a chapter “Mesomerie- This is deductive learning, learning from a theory to produce 27 Lehre” for a textbook on laboratory experiments.[1] It should, data. 28 however, be recognized that the concepts of inductive and However, it was also realized that a computer operates on a 29 resonance effect contained in this chapter were not derived bit level and thus can be used for logical operations. 30 from any theory but were an attempt to order the observations Furthermore, software can be developed that allow the 31 and data on product distributions and reaction rates in electro- processing of data and information. Thus, computers can be 32 philic aromatic substitution. used for inductive learning (Figure 1): data can be put together 33 By doing experiments, chemists have amassed a huge to generate information and many pieces of information can be 34 amount of data on chemical structures and their properties. In generalized to produce knowledge. 35 1971, about one million substances were registered in the As an example, the measurement of the biological activity 36 Chemical Abstracts Service STN database and our supervisor of a compound is only of much use when the structure of the 37 Rolf Huisgen gave us the impression that he knew whether a compound is known; this is then information, putting the 38 compound was known or not–and we could not find a case activity data in the context of the chemical structure. Several 39 where he was wrong. While this may have been feasible with sets of structures and their corresponding biological activities 40 one million compounds it is definitely not possible any more 41 now with 160 million organic and inorganic substances and 68 42 million protein and nucleic acid sequences in the CAS database. 43 This review will show how the methods of chemoinfor- 44 matics have made accessible this huge amount of data and 45 information and how these data can be converted into knowl- 46 edge to increase our understanding of chemistry and to 47 48 49 [a] Prof. Dr. J. Gasteiger 50 Computer-Chemie-Centrum and Institute of Organic Chemistry 51 University of Erlangen-Nuremberg 52 Naegelsbachstrasse 25, 91052 Erlangen, Germany E-mail: [email protected] 53 © 2020 The Authors. Published by Wiley-VCH GmbH. This is an open 54 access article under the terms of the Creative Commons Attribution Non- 55 Commercial License, which permits use, distribution and reproduction in 56 any medium, provided the original work is properly cited and is not used for commercial purposes. Figure 1. Deductive and inductive learning. 57 ChemPhysChem 2020, 21, 2233– 2242 www.chemphyschem.org 2234 © 2020 The Authors. Published by Wiley-VCH GmbH Wiley VCH Montag, 05.10.2020 2020 / 179989 [S. 2234/2242] 1 Reviews ChemPhysChem doi.org/10.1002/cphc.202000518 can then be analyzed and generalized to produce an under- were, and still are being, established.[5,6] The challenge of 1 standing, knowledge, of the relationships between structure computer-assisted synthesis design was taken up early on.[7] 2 and biological activity. The third question needs automatic procedures for structure 3 elucidation.[8,9] 4 These fundamental questions of a chemist have been the 5 2.1. Chemoinformatics driving forces of a lot of work in chemoinformatic in the last 6 few decades which will be reported in some of the following 7 Starting in the 1960s, computer methods were developed that chapters. 8 allowed one to perform inductive learning in chemistry, a field 9 that later became known as Chemoinformatics.[2,3] First, meth- 10 ods had to be developed for the computer representation of 2.2. Artificial Intelligence 11 chemical structures and reactions. Then, procedures had to be 12 utilized or developed for inductive learning, a field that was It was realized from early on that the development of systems 13 coined as chemometrics and encompassed methods from for property prediction, synthesis design, or structure elucida- 14 statistics and pattern recognition. Chemometrics methods were tion are quite demanding tasks and would require a lot of 15 applied to the analysis of data from analytical chemistry.[4] conceptual work and state of the art computer technology. 16 However, work was also initiated to tackle quite difficult tasks Therefore, emerging methods from computer science found 17 such as those embedded in the fundamental questions of a their early applications in chemistry. This is true for methods 18 chemist: that were subsumed under the name of artificial intelligence 19 1) What structure do I need for a desired property? and publications with titles such as “Applications of Artificial 20 2) How can I synthesize this structure? Intelligence for Chemical Inference” appeared in the context of 21 3) What is the outcome of my reaction? the DENDRAL project at Stanford University.[10] The DENDRAL 22 For answering a question on property predictions quantita- project developed methods for predicting the structure of a 23 tive structure property/activity relationships (QSPR and QSAR) compound from its mass spectrum. In spite of the collaboration 24 of some highly reputed chemists and computer scientists and a 25 lot of work put into its development, the DENDRAL project was 26 Johann Gasteiger has studied chemistry at eventually discontinued. Many reasons might be found for that 27 the Ludwig-Maximilians-University of Munich decision, not the least that the field of artificial intelligence had 28 and the University of Zürich. He obtained his lost its promise and reputation in the late 1970s. In recent years, 29 PhD in 1971 at the University of Munich under the guidance of Prof. Dr. Rolf Huisgen a renaissance of artificial intelligence in general and of its 30 with studies on the chemistry of cyclooctate- application in chemistry, in particular, can be observed. Several 31 traene and homotropylium ions. He then reasons have contributed to this development: availability of 32 went as a postdoc to Prof. Andrew Streitwies- large amounts of data, increase in computer power, and new 33 er Jr. at the University of California in Berkeley methods for processing these data. As these methods all are 34 where he did ab initio calculations on carbanions. In 1972 he joined the research based on computer processing this field has also often been 35 group of Prof. Ivar Ugi at the Technical referred to “machine learning”. There is no clear distinction 36 University of Munich, working on a computer between these two terms although the term artificial intelli- 37 program for the planning of organic synthe- gence seems to be the more comprehensive one. 38 ses.