Introduction:

Computer-aided drug design (CADD) stands at the forefront of modern drug discovery, utilising computational methods to unveil, optimise, and evaluate potential drug candidates. This multidisciplinary approach is categorised into structure-based drug design (SBDD) and ligand-based drug design (LBDD), each aiming to identify molecules with high affinity, specificity, and favourable pharmacokinetic and pharmacodynamic properties.

Challenges in CADD:

The vast diversity and complexity of the chemical space pose a significant challenge in CADD, given the staggering number of potential drug-like molecules. To surmount this obstacle, researchers have developed consensus methods, merging various CADD techniques such as docking, scoring, pharmacophore modelling, QSAR, machine learning, and artificial intelligence. Consensus methods play a pivotal role in improving accuracy, reliability, and robustness by integrating multiple sources of information and reducing bias and uncertainty.

Recent Applications and Developments:

This blog delves into the recent applications and developments of consensus methods in CADD, with a particular focus on their role in addressing the challenges posed by the COVID-19 pandemic. We will explore how consensus methods have been instrumental in identifying novel inhibitors targeting various key proteins involved in the infection and replication of SARS-CoV-2.

Consensus Docking:

Among the widely adopted consensus methods, consensus docking takes centre stage. It involves applying multiple docking programs and scoring functions to rank ligands’ binding affinity and pose to a target protein. This method has successfully identified novel inhibitors for COVID-19 drug targets such as the main protease (Mpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp), and the S protein. Examples include a study screening 1.3 billion compounds against Mpro and another screening 6899 FDA-approved drugs against PLpro, both showcasing the efficacy of consensus docking in virtual screening campaigns.

Consensus Pharmacophore:

Another powerful consensus method is consensus pharmacophore, which generates and combines multiple pharmacophore models based on different sets of active ligands or different methods of pharmacophore generation. This approach has been applied to discover novel inhibitors of COVID-19 drug targets, such as the S protein-ACE2 interaction, RdRp, and Mpro. Studies screening natural products and FDA-approved drugs have identified potential candidates with high binding affinity and stability, illustrating the versatility of consensus pharmacophores.

Consensus QSAR:

The third consensus method, consensus QSAR, involves developing and integrating multiple quantitative structure-activity relationship (QSAR) models. This approach improves the predictive power and applicability domain of the models, enhancing confidence and reliability. Consensus QSAR has been employed to discover novel inhibitors of COVID-19 drug targets, with studies screening natural products against Mpro and FDA-approved drugs against RdRp.

Consensus Machine Learning:

The fourth consensus method, consensus machine learning, integrates multiple machine learning algorithms and models to predict the activity, toxicity, or binding affinity of drug candidates. This approach leverages the strengths of individual algorithms, increasing generalisation and reliability. Consensus machine learning has been applied to identify inhibitors for COVID-19 drug targets, as seen in studies screening natural products against Mpro and FDA-approved drugs against RdRp.

Advantages, Limitations, and Future Directions:

The blog will also delve into the advantages and limitations of consensus methods, shedding light on their potential and areas of improvement. Furthermore, we will discuss future directions and challenges for CADD research, emphasising the evolving landscape of computational drug design in the pursuit of effective treatments.

References

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