At the beginning of the twenty-first century, after the sequencing of the human genome, exploring protein products from the genome with a chemical perspective was proposed, giving rise to a new interdisciplinary research field named chemogenomics. This research field aims towards the systematic identification of small molecules that interact with proteins targets and modulate their function. Chemogenomics is a crucial discipline in pharmacological research and drug discovery, as it allows the identification of novel bioactive compounds and therapeutic targets, as well as the elucidation of the mechanism of action of known drugs.
The ultimate goal of chemogenomics is, in principle, the identification of all small molecules that are capable of interacting with any biological target. However, considering the number of existing small molecules and biological targets, this task is impossible to achieve experimentally. Developments in computer science-related disciplines, such as cheminformatics, molecular modelling, and artificial intelligence have made possible the in silico analysis of millions of potential interactions between small molecules and biological targets, prioritizing on a rational basis the experimental tests to be performed, reducing with that the time and costs associated with them. These computational approaches represent the toolbox of a research field known as computational chemogenomics.
Methods from computational chemogenomics have become more and more important in pharmacological research and drug discovery. Advances in computer science and artificial intelligence, as well as the growing availability of experimental data, has opened the door to the development of new computational models and algorithms. These models require thorough validation and dissemination within the scientific community. This Research Topic aims to present recent advances and applications of computational chemogenomics in pharmacological research and drug discovery.
We welcome review and original research submissions from researchers in the field of computational chemogenomics, related to the development, validation, and successful applications of in silico approaches to pharmacological research and drug discovery. The areas to be covered in this Research Topic include, but are not limited to, the following:
Applications of computational chemogenomics:
-Target identification
-Discovery of bioactive compounds
-Design of small molecule libraries
-Multi-target drug discovery
-Drug repurposing
-In silico toxicology
Method development of computational chemogenomics:
-Virtual screening (including target fishing)
-De novo design
-Machine learning
-Artificial intelligence
-Data mining and visualization
In the interest of open science, data reproducibility, and to expand the potential of the reported research, we encourage
authors to make their codes and experimental data available to the public.
Note to Authors: Manuscripts based solely on in silico techniques without experimental data or lack of rigorous
validation will not be considered for review.
At the beginning of the twenty-first century, after the sequencing of the human genome, exploring protein products from the genome with a chemical perspective was proposed, giving rise to a new interdisciplinary research field named chemogenomics. This research field aims towards the systematic identification of small molecules that interact with proteins targets and modulate their function. Chemogenomics is a crucial discipline in pharmacological research and drug discovery, as it allows the identification of novel bioactive compounds and therapeutic targets, as well as the elucidation of the mechanism of action of known drugs.
The ultimate goal of chemogenomics is, in principle, the identification of all small molecules that are capable of interacting with any biological target. However, considering the number of existing small molecules and biological targets, this task is impossible to achieve experimentally. Developments in computer science-related disciplines, such as cheminformatics, molecular modelling, and artificial intelligence have made possible the in silico analysis of millions of potential interactions between small molecules and biological targets, prioritizing on a rational basis the experimental tests to be performed, reducing with that the time and costs associated with them. These computational approaches represent the toolbox of a research field known as computational chemogenomics.
Methods from computational chemogenomics have become more and more important in pharmacological research and drug discovery. Advances in computer science and artificial intelligence, as well as the growing availability of experimental data, has opened the door to the development of new computational models and algorithms. These models require thorough validation and dissemination within the scientific community. This Research Topic aims to present recent advances and applications of computational chemogenomics in pharmacological research and drug discovery.
We welcome review and original research submissions from researchers in the field of computational chemogenomics, related to the development, validation, and successful applications of in silico approaches to pharmacological research and drug discovery. The areas to be covered in this Research Topic include, but are not limited to, the following:
Applications of computational chemogenomics:
-Target identification
-Discovery of bioactive compounds
-Design of small molecule libraries
-Multi-target drug discovery
-Drug repurposing
-In silico toxicology
Method development of computational chemogenomics:
-Virtual screening (including target fishing)
-De novo design
-Machine learning
-Artificial intelligence
-Data mining and visualization
In the interest of open science, data reproducibility, and to expand the potential of the reported research, we encourage
authors to make their codes and experimental data available to the public.
Note to Authors: Manuscripts based solely on in silico techniques without experimental data or lack of rigorous
validation will not be considered for review.