Computer-aided drug design (CADD) has transformed modern drug discovery by employing computer techniques to find, develop, and assess biologically active compounds. It serves as a potent tool in expediting the early stages of chemical development and drug discovery processes. The techniques and tools utilized in CADD permeate all stages of the drug discovery pipeline, facilitating the identification and design of potential drug candidates. One of the primary advantages of CADD is its ability to predict the interactions between small molecules and biological targets with high accuracy. This predictive capability is achieved through the use of various computational techniques, including molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) modeling.
Over the past two decades, advancements in drug design protocols, coupled with increased computational power, have enabled scientists to effectively generate disease-oriented solutions at reduced costs and within feasible timeframes. Advancing our collective understanding of computer-aided drug discovery and development is paramount for accelerating the identification of novel therapeutics, optimizing drug properties, and minimizing the time and resources required for drug development. Recent breakthroughs underscore the potential of artificial intelligence (AI) and machine learning in predicting molecular interactions, identifying drug targets, and optimizing lead compounds, thereby streamlining drug discovery processes and reducing associated costs. Furthermore, the integration of predictive toxicology models has bolstered the safety assessment of drug candidates, mitigating late-stage failures in drug development.
This Collection aims to compile research that encompasses various aspects of computer-aided drug design, molecular dynamics, virtual screening, computer modeling, and predictive toxicology, highlighting the latest advancements and methodologies in the field. Topics of interest include but are not limited to:
- AI and machine learning in drug development
- Molecular dynamics simulations
- Molecular docking
- Predictive toxicology in drug development
- Virtual screening strategies
- Virtual library design
- Quantitative structure-activity relationship modeling
- High-throughput screening
- Lead optimization techniques
- De novo design methodologies: ligand based drug design and structure based drug design
- Other computational approaches within the realm of CADD
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