To date, the program for the development of methods and models for atomistic and continuum simulation directed toward chemicals and materials has reached an incredible degree of sophistication and maturity. Currently, one can witness an increasingly rapid emergence of advances in computing, artificial intelligence, and robotics. This drives us to consider the future of computer simulation of matter from the molecular to the human length and time scales in a radical way that deliberately dares to go beyond the foreseeable next steps in any given discipline. This perspective article presents a view on this future development that we believe is likely to become a reality during our lifetime.

The Matter Simulation (R)evolution

There is no particular point in time that determines when chemoinformatics was founded or established. It slowly evolved from several, often quite humble beginnings. Scientists in various fields of chemistry struggled with the development of computer methods which allowed them to manage the enormous amount of chemical information and to find relationships between the structure and properties of a compound. During the 1960s some early developments appeared that led to a flurry of activities in the 1970s. This review provides a general overview of basic methods in the specific fields of chemoinformatics, from encoding chemical compounds, storing and searching data in databases, to generating and analyzing these data. In addition, the chief interconnecting points of chemoinformatics applications are highlighted including the contributions of Johann Gasteiger to this field.

Basic Overview of Chemoinformatics

Die Leistungen des menschlichen Gehirns haben seit jeher fasziniert und Anlas gegeben, ihre Grundlagen zu erforschen. In den letzten funfzig Jahren wurde eine Reihe von Modellen entwickelt, die Teilfunktionen des Gehirns nachvollziehen sollen. Die parallel dazu verlaufende Entwicklung von Computern ging ganz andere Wege. Dies hat zur Folge, das die derzeitigen Computerarchitekturen und Betriebssysteme sowie die Programmabarbeitung sehr wenig mit der Informationsverarbeitung im Gehirn gemeinsam haben. Gegenwartig findet eine Ruckbesinnung auf die Leistungen des Gehirns statt, und die Modelle fur die Informationsverarbeitung im Gehirn wurden in Algorithmen gefast und breit zuganglich gemacht. Zentraler Bestandteil dieser Gehirnmodelle, der neuronalen Netze, ist eine Informationsverarbeitungseinheit, das Modell eines Neurons. In einem solchen kunstlichen Neuron werden recht einfache mathematische Operationen ausgefuhrt; die Leistungsfahigkeit eines neuronalen Netzes kommt erst durch das Zusammenschalten vieler Neuronen in einem Netzwerk zustande. So wie die einzelnen neuronalen Netze verschiedene Leistungen des Gehirns nachvollziehen, so konnen sie auch zur Losung unterschiedlicher Problemtypen eingesetzt werden, zur Klassifikation von Objekten, zum Modellieren funktionaler Zusammenhange, zur Speicherung und zum Ruckruf von Information sowie zur Abbildung einer Menge von Daten. Dieses Potential bietet auch fur chemische Informationen viele Verarbeitungsmoglichkeiten; die bisherigen Anwendungen uberstreichen bereits ein weites Feld: Analyse spektroskopischer Daten, Vorhersage von Reaktionen, Kontrolle chemischer Prozesse und Analyse elektrostatischer Potentiale stehen stellvertretend fur viele andere Anwendungen.

Neuronale netze in der chemie

Graph Theory in Chemistry

Organic reactions can be run under a variety of conditions, from laboratory experiments, through technical processes, to combinatorial chemistry The scope is further extended when the metabolism of compounds and the reactions in the mass spectrometer are included We present here several concepts:  reactors, phases, and modes, which, together with a kinetic modeling, allow the treatment of such a broad scope of organic reactions These concepts have been implemented in a knowledge-based system, EROS Several applications of this system to the wide world of organic reactions are given

Simulation of organic reactions: from the degradation of chemicals to combinatorial synthesis

Systems for the prediction of organic reactions and the simulation of mass spectra are presented. In addition, methods and systems for the automatic knowledge acquisition for such systems are highlighted.

Models for the Representation of Knowledge About Chemical Reactions

Computer‐Assisted Reaction Prediction and Synthesis Design

Automated Derivation of Reaction Rules for the EROS 6.0 System for Reaction Prediction

A Collection of Computer Methods for Synthesis Design and Reaction Prediction

Computer‐Assisted Prediction of the Degradation of Chemicals: Hydrolysis of Amides and Benzoylphenylureas.

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