Lib-INVENT: Reaction Based Generative Scaffold Decoration for in silico Library Design

Due to the strong relationship between desired molecular activity to its structural core, screening of focused, core sharing chemical libraries is a key step in lead optimisation. Despite the plethora of current research focused on in silico methods for molecule generation, to our knowledge, no tool capable of designing such libraries has been proposed. In this work, we present a novel tool for de novo drug design called Lib-INVENT. This is capable of rapidly proposing chemical libraries of compounds sharing the same core while maximising a range of desirable properties. To further help the process of designing focused libraries, the user can list specific chemical reactions that can be used for the library creation. Lib-INVENT is therefore a flexible tool for generating virtual chemical libraries for lead optimisation in a broad range of scenarios. Additionally, the shared core ensures that the compounds in the library are similar, possessing desirable properties and can be also synthesized under the same or similar conditions.

Lib-INVENT: Reaction Based Generative Scaffold Decoration for in silico Library Design

Due to the strong relationship between desired molecular activity to its structural core, screening of focused, core sharing chemical libraries is a key step in lead optimisation. Despite the plethora of current research focused on in silico methods for molecule generation, to our knowledge, no tool capable of designing such libraries has been proposed. In this work, we present a novel tool for de novo drug design called Lib-INVENT. This is capable of rapidly proposing chemical libraries of compounds sharing the same core while maximising a range of desirable properties. To further help the process of designing focused libraries, the user can list specific chemical reactions that can be used for the library creation. Lib-INVENT is therefore a flexible tool for generating virtual chemical libraries for lead optimisation in a broad range of scenarios. Additionally, the shared core ensures that the compounds in the library are similar, possessing desirable properties and can be also synthesized under the same or similar conditions.

RedDB, a Computational Database of Electroactive Molecules for Aqueous Redox Flow Batteries

An increasing number of electroactive compounds have recently been explored for their use in high-performance redox flow batteries for grid-scale energy storage. Given the vast and highly diverse chemical space of the candidate compounds, it is alluring to access their physicochemical properties in a speedy way. High-throughput virtual screening approaches, which use powerful combinatorial techniques for systematic enumerations of large virtual chemical libraries and respective property evaluations, are indispensable tools for an agile exploration of the designated chemical space. Herein, RedDB: a computational database that contains 31,677 molecules from two prominent classes of organic electroactive compounds, quinones and aza-aromatics, has been presented. RedDB incorporates miscellaneous physicochemical property information of the compounds that can potentially be employed as battery performance descriptors. RedDB’s development steps, including: i)chemical library generation, ii) molecular property prediction based on quantum chemical calculations, iii) aqueous solubility prediction using machine learning, and iv) data processing and database creation, have been described.

RedDB, a Computational Database of Electroactive Molecules for Aqueous Redox Flow Batteries

An increasing number of electroactive compounds have recently been explored for their use in high-performance redox flow batteries for grid-scale energy storage. Given the vast and highly diverse chemical space of the candidate compounds, it is alluring to access their physicochemical properties in a speedy way. High-throughput virtual screening approaches, which use powerful combinatorial techniques for systematic enumerations of large virtual chemical libraries and respective property evaluations, are indispensable tools for an agile exploration of the designated chemical space. Herein, RedDB: a computational database that contains 31,677 molecules from two prominent classes of organic electroactive compounds, quinones and aza-aromatics, has been presented. RedDB incorporates miscellaneous physicochemical property information of the compounds that can potentially be employed as battery performance descriptors. RedDB’s development steps, including: i)chemical library generation, ii) molecular property prediction based on quantum chemical calculations, iii) aqueous solubility prediction using machine learning, and iv) data processing and database creation, have been described.

Specific Targeting of Plant and Apicomplexa Parasite Tubulin through Differential Screening Using In Silico and Assay-Based Approaches

Dinitroanilines are chemical compounds with high selectivity for plant cell α-tubulin in which they promote microtubule depolymerization. They target α-tubulin regions that have diverged over evolution and show no effect on non-photosynthetic eukaryotes. Hence, they have been used as herbicides over decades. Interestingly, dinitroanilines proved active on microtubules of eukaryotes deriving from photosynthetic ancestors such as Toxoplasma gondii and Plasmodium falciparum, which are responsible for toxoplasmosis and malaria, respectively. By combining differential in silico screening of virtual chemical libraries on Arabidopsis thaliana and mammal tubulin structural models together with cell-based screening of chemical libraries, we have identified dinitroaniline related and non-related compounds. They inhibit plant, but not mammalian tubulin assembly in vitro, and accordingly arrest A. thaliana development. In addition, these compounds exhibit a moderate cytotoxic activity towards T. gondii and P. falciparum. These results highlight the potential of novel herbicidal scaffolds in the design of urgently needed anti-parasitic drugs.

A Hybrid Approach Based on Self-Organizing Neural Networks and the K-Nearest Neighbors Method to Study Molecular Similarity

The “Molecular Similarity Principle” states that structurally similar molecules tend to have similar properties—physicochemical and biological. The question then is how to define “structural similarity” algorithmically and confirm its usefulness. Within this framework, research by similarity is registered, which is a practical approach to identify molecule candidates (to become drugs or medicines) from databases or virtual chemical libraries by comparing the compounds two by two. Many statistical models and learning tools have been developed to correlate the molecules’ structure with their chemical, physical or biological properties. The role of data mining in chemistry is to evaluate “hidden” information in a set of chemical data. Each molecule is represented by a vector of great dimension (using molecular descriptors), the applying a learning algorithm on these vectors. In this paper, the authors study the molecular similarity using a hybrid approach based on Self-Organizing Neural Networks and Knn Method.

A Hybrid Approach Based on Self-Organizing Neural Networks and the K-Nearest Neighbors Method to Study Molecular Similarity

The “Molecular Similarity Principle” states that structurally similar molecules tend to have similar properties—physicochemical and biological. The question then is how to define “structural similarity” algorithmically and confirm its usefulness. Within this framework, research by similarity is registered, which is a practical approach to identify molecule candidates (to become drugs or medicines) from databases or virtual chemical libraries by comparing the compounds two by two. Many statistical models and learning tools have been developed to correlate the molecules’ structure with their chemical, physical or biological properties. The role of data mining in chemistry is to evaluate “hidden” information in a set of chemical data. Each molecule is represented by a vector of great dimension (using molecular descriptors), the applying a learning algorithm on these vectors. In this paper, the authors study the molecular similarity using a hybrid approach based on Self-Organizing Neural Networks and Knn Method.

Molecular Similarity

In order to identify new molecules susceptible to become medicines, the pharmaceutical research has more and more resort to new technologies to synthesize big number of molecules simultaneously and to test their actions on given therapeutic target. This data can be exploited to construct the models permitting to predict the properties of molecules not yet tested, even not yet synthesized. Such predictive models are very important because they make it possible to suggest the synthesis of new molecules, and to eliminate very early in the the molecule’s search process the molecules whose properties would prevent their use as medicine. The authors call it virtual sifting. It is within this framework that research by similarity is registered. It is a practical approach to identify molecules candidates (to become medicines) from the data bases or the virtual chemical libraries by comparing the compounds two by two. Many statistical models and learning tools have been developed to correlate the molecule’s structure with their chemical, physical or biological properties. The large majority of these methods start by transforming each molecule in a vector of great dimension (using molecular descriptors), then use a learning algorithm on these vectorial descriptions. The objective of this chapter is to study molecular similarity using a particular type of neural networks: the Kohonen networks (also called “SOM” Self- Organizing Maps), applying the nearest neighbor algorithm to the projection of the molecules (coordinates) in the constructed MAP.