In Silico Drug Design of Chikungunya Antivirals: Using Consensus Methods for Virtual Screening
Abstract
Structure-based virtual screening is an attractive strategy in drug design. It enables the selection of potential binders to target biomolecules with known molecular structures, providing a rational framework for studying their mechanisms of action and guiding further optimization. However, accurately predicting the free energy of binding remains an extremely challenging task, and the available tools only provide approximate estimates. In this context, one strategy to improve the predictive power of virtual screening and increase the probability of identifying true binders is to employ multiple docking algorithms and combine their results using a consensus method. In this chapter, we will describe a step-by-step methodology to perform this task. We will conduct a virtual screening using AutoDock Vina, AutoDock4, and LeDock, and then combine their results employing the Exponential Consensus Ranking algorithm. Finally, we will discuss possible extensions of this strategy, such as incorporating Molecular Dynamics simulations as an additional filter in the virtual screening or integrating the novel Artificial Intelligence-based methodologies into the consensus framework.
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