De novo design of potential RecA inhibitors using multi objective optimization

Targeting the bacterial SOS response for new antimicrobial agents: drug targets, molecular mechanisms and inhibitors

Antimicrobial resistance is a pressing threat to global health, with multidrug-resistant pathogens becoming increasingly prevalent. The bacterial SOS pathway functions in response to DNA damage that occurs during infection, initiating several pro-survival and resistance mechanisms, such as DNA repair and hypermutation. This makes SOS pathway components potential targets that may combat drug-resistant pathogens and decrease resistance emergence. This review discusses the mechanism of the SOS pathway; the structure and function of potential targets AddAB, RecBCD, RecA and LexA; and efforts to develop selective small-molecule inhibitors of these proteins. These inhibitors may serve as valuable tools for target validation and provide the foundations for desperately needed novel antibacterial therapeutics.

Computational Approaches for De Novo Drug Design: Past, Present, and Future

Drug discovery is time- and resource-consuming. To this end, computational approaches that are applied in de novo drug design play an important role to improve the efficiency and decrease costs to develop novel drugs. Over several decades, a variety of methods have been proposed and applied in practice. Traditionally, drug design problems are always taken as combinational optimization in discrete chemical space. Hence optimization methods were exploited to search for new drug molecules to meet multiple objectives. With the accumulation of data and the development of machine learning methods, computational drug design methods have gradually shifted to a new paradigm. There has been particular interest in the potential application of deep learning methods to drug design. In this chapter, we will give a brief description of these two different de novo methods, compare their application scopes and discuss their possible development in the future.

Design of multi-drug combinations for poly-pharmacological effects using composition-activity relationship modeling and multi-objective optimization approach: Application in traditional Chinese medicine

In recent years, multi-component therapies are increasingly utilized to treat complex diseases such as cancer, diabetes, and other chronic complex diseases. Here, we proposed the protocol for rational design of drug combination with poly-pharmacological effects by integration of design of experiments (DOE), computational modeling, and multiple-objective optimization algorithm. Here, we introduce a common workflow for modeling quantitative relationship of chemical composition and multiple activities of drug combinations. As an example, anti-oxidation, neuroprotective, and anti-platelet activities of three different salvia polyphenols were measured, which were mathematically represented by multivariant regression models to evaluate the effect of combination. In validation, the optimized combination which obtained by weighed-sum method showed good activities in all three models. Our results demonstrate that the multiple-objective optimization strategy is suitable to optimize the ratio of the compounds so to induce the best therapeutic action.

The Use of Biosensors to Explore the Potential of Probiotic Strains to Reduce the SOS Response and Mutagenesis in Bacteria

A model system based on the Escherichia coli MG1655 (pRecA-lux) Lux-biosensor was used to evaluate the ability of the fermentates of eight probiotic strains to reduce the SOS response stimulated by ciprofloxacin in bacteria and mutagenesis mediated by it. Preliminary attempts to estimate the chemical nature of active components of the fermentates were conducted.

Multi-objective optimization methods in drug design

Drug discovery is a challenging multi-objective problem where numerous pharmaceutically important objectives need to be adequately satisfied for a solution to be found. The problem is characterized by vast, complex solution spaces further perplexed by the presence of conflicting objectives. Multi-objective optimization methods, designed specifically to address such problems, have been introduced to the drug discovery field over a decade ago and have steadily gained in acceptance ever since. This paper reviews the latest multi-objective methods and applications reported in the literature, specifically in quantitative structure–activity modeling, docking, de novo design and library design. Further, the paper reports on related developments in drug discovery research and advances in the multi-objective optimization field.