This review is dedicated to a survey on molecular similarity and diversity. Key findings reported in recent investigations are selectively highlighted and summarized. Even if this overview is mainly centered in chemoinformatics, applications in other areas (pharmaceutical and medical chemistry, combinatorial chemistry, chemical databases management, etc.) are also introduced. The approaches used to define and describe the concepts of molecular similarity and diversity in the context of chemoinformatics are discussed in the first part of this review. We introduce, in the second and third parts, the descriptions and analyses of different methods and techniques. Finally, current applications and problems are enumerated and discussed in the last part.

Predictive modeling has become a practical research tool in homogeneous catalysis. It can help to pinpoint 'good regions' in the catalyst space, narrowing the search for the optimal catalyst for a given reaction. Just like any other new idea, in silico catalyst optimization is accepted by some researchers and met with skepticism by others. The basic requirements for good predictive models are a reliable set of initial experimental data, a method for generating and testing virtual catalyst libraries, and robust validation protocols. Once you have these, the key task is translating thecatalysis problems into something that a computer can understand. In this tutorial review we explain in simple terms what predictive modeling actually is, why and when should one use it, and how it can be implemented.