Drug research and development is a complicated, time-consuming, and costly process. There exists no single way through which drug discovery takes place. Each team taking part in drug discovery has its own strengths and weaknesses.
To increase strengths and reduce weaknesses, everyone must improve their ability to extract added value from the already available information. The amount of proteins that may become pharmacological targets exceeded several thousand; taking into account the alternative splicing, posttranslational modifications and protein-protein interactions this number increased to several millions; number of already synthesized small organic molecules that may be used as ligands inhibiting or stimulating those targets is more than sixty millions; the number of virtual molecules generated in computer surpassed hundred billions.
Effective utilization of this information requires application of computational methods, which allow selection of targets and their ligands most promising for treatment of certain disease.
Working in this field for more than twenty years, we developed and validated a few predictive tools that currently available as separate web resources. Our long-term goal is not just improve and extend these tools but integrate their functionality providing better services for particular needs of researchers working in the field of drug discovery.