Frontiers in Computational Chemistry

Computer-aided drug discovery and development (CADD) has emerged as a transformative approach in the pharmaceutical industry, revolutionizing the traditional drug development process. This abstract provides a comprehensive overview of the latest advancements, methodologies, and applications in CADD. The first section outlines the fundamental principles of CADD, emphasizing its integration of computational techniques, algorithms, and databases to expedite the identification of potential drug candidates. Molecular modeling, virtual screening, and quantitative structure-activity relationship (QSAR) analysis are highlighted as primary techniques used to predict ligand-target interactions and optimize drug properties. The second section discusses the role of machine learning (ML) and artificial intelligence (AI) in CADD, showcasing their capability to analyze vast datasets, identify patterns, and predict novel drug-target interactions with unparalleled accuracy. ML algorithms, such as deep learning, have shown promising results in de novo drug design, target identification, and toxicity prediction. In the third section, the application of CADD in various stages of drug discovery and development is explored. From hit identification and lead optimization to pharmacokinetic/pharmacodynamic (PK/PD) modeling and clinical trial design, CADD tools streamline decision-making processes, reduce costs, and accelerate the development timeline. Furthermore, this chapter addresses the challenges and future prospects of CADD. Despite its remarkable achievements, CADD still faces limitations, such as the accurate representation of biological systems and the integration of multi-scale modeling approaches. Additionally, ethical considerations regarding data privacy, intellectual property rights, and regulatory compliance remain pivotal in the widespread adoption of CADD methodologies

Drug repurposing, or repositioning, is a key strategy in biomedical innovation, leveraging existing approved drugs for new therapeutic uses. This approach significantly cuts development costs and shortens the lengthy traditional drug approval timelines. This approach is especially valuable for rare diseases, addressing unmet needs by overcoming the high costs and challenges of developing new treatments. Drug repurposing optimises drug utility and strategically allocates limited research resources. In silico techniques have unlocked extraordinary opportunities in this domain, offering a pathway to identify and validate new therapeutic indications. This can expand treatment options and greatly improve the precision of targeted therapies. The field of drug development has undergone an enormous shift with the introduction of in silico techniques. Advanced computational techniques, such as artificial intelligence (AI), machine learning (ML), and chemo-informatics, have driven a paradigm shift in identifying and developing new drug applications. These technologies use vast databases and advanced bioinformatics to uncover elusive drug-target interactions. Tools like Reactome and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) have proven to unravel the complex interactions governing drug efficacy. The focus on a holistic approach, integrating diverse sets of biological, clinical, and epidemiological data, has been instrumental in opening new avenues for repurposing opportunities. Success stories highlight the impact of in silico drug repurposing, showcasing its role in meeting unmet medical needs and transforming therapeutic development. While in silico drug repurposing prospects are undeniably promising, the field is not without its challenges. The conclusion explores current challenges and potential solutions, highlighting how innovative computational approaches can revolutionise drug development, enhancing efficiency, cost-effectiveness, and speed. The ultimate aim is to advance personalised medicine and improve patient care with unprecedented precision.

Peptides have emerged as promising candidates in therapeutics and diagnostics due to their unique properties. They offer advantages over traditional small molecule drugs, including high specificity, reduced off-target effects, biocompatibility, and biodegradability. However, peptide-based therapeutics also present challenges of low stability, delivery, synthesis, membrane permeability, and oral availability. Many strategies such as cyclisation, incorporation of N- and C- terminal protecting groups, and non-standard amino acid design of appropriate peptide delivery systems have been adopted to mitigate these challenges. Computational techniques enable faster design and development and reduce experimental costs involved in drug discovery and design and therefore, have gained prominence for in-silico testing and development of peptides. This chapter explores methods involved in the computational design of therapeutic peptides, with significant attention to peptide-specific molecular docking tools, lead optimization strategies, and Molecular Dynamics (MD) simulations. We also discuss the applicability of peptides in biomedicine and review specialized peptide databases, exemplified by the case study of the PepEngine. A compendium of Machine Learning (ML) tools used in peptide drug design highlights the latest advances in the field. Peptide-based therapeutics are highly promising due to their lower bioaccumulation and toxicity along with high specificity. Many peptide-based drugs, such as insulin, oxytocin, and enfuvirtide, have been widely accepted for therapeutic applications. By mitigating the challenges faced in peptide design and aiding in the development of novel therapeutic peptides, computational approaches have played an instrumental role in the peptide drug development process. 

Drug development is a critical endeavor within the pharmaceutical sector. Integrating computational approaches has significantly reduced both the time and costs associated with discovering new drugs. This chapter starts by highlighting the pivotal role of multiscale molecular simulations in determining drug-binding sites on target macromolecules and elucidating the mechanisms underlying drug actions. It then delves into molecular dynamics (MD) simulation methods, focusing on drug design strategies based on structure and ligand considerations. Additionally, the chapter explores the development of advanced analysis tools and the integration of machine learning techniques, which collectively enhance the efficiency of the drug discovery process. Traditional MD analysis methods, such as root mean square deviation (RMSD) of backbone atoms, root mean square fluctuation (RMSF), radius of gyration, and interaction analyses, are extensively used to monitor structural changes and convergence during simulations. Beyond these, newer trajectory mapping methods offer intuitive and conclusive ways to visualize protein simulations by plotting the protein's backbone movements as heat maps. Molecular dynamics simulations utilize physical algorithms to model chemical systems and compute atomic and molecular properties. In drug design and discovery, computational chemistry methods are employed to predict mechanisms such as drug binding to targets and the chemical properties of potential drug candidates. The combined use of traditional and novel analysis methods is anticipated to have wide applications in deriving meaningful insights from protein MD simulations across fields like structural biology, biochemistry, and pharmaceutical research. The chapter concludes with several case studies and success stories demonstrating the application of MD simulations as a powerful computer-aided drug discovery tool in diabetes and Alzheimer's treatments.Highlighted examples include achievements in anticancer, antibacterial, antileishmaniasis, and antiviral drug design, showcasing the impact of in silico drug design in developing innovative therapies.

The computational prediction of protein-ligand binding affinities has become a key step in the successful virtual screening of compounds for drug development and discovery. However, consistently accurate protein-ligand binding affinity calculations are challenging in part due to, 1) the large protein/ligand conformational space that must be sampled/searched, 2) the inconsistent accuracy of classical molecular mechanics potentials, commonly used to compute binding affinities, especially when π-stacking, halogen interaction, or metal centers are present, or when polarization or charge transfer is significant. In this chapter, recent advances in quantum mechanical methods that facilitate their application to protein-ligand binding free energy calculations are discussed, with an emphasis on fragmentation methods and their combination with conformational search algorithms. The accuracy of these new approaches with respect to the prediction of protein-ligand binding free energy is evaluated. New tools to improve workflow and speed up calculations are also discussed.

Inflammation is the body's response against an aggressive chemical, physical, or biological agent . Despite being a natural response, if exaggerated , it can damage the organism, making necessary pharmacology interventions. Several drugs can control inflammation , such as COX inhibitors . However , there is a high incidence of side effects. It is well-established that the assembly of NLRP3 triggers an inflammatory response, leading to various diseases and highlighting its significance as a therapeutic target. Discovering new drugs and potential targets is urgent to overcome these limitations . Inflammasomes such as NLRP3 constituting the innate immune responses, leading to the production of pro-inflammatory cytokines, such as IL-1β and TNFα. Thus, targeting NLRP3 can provide a new anti-inflammatory drug that is safe and free of the COX inhibitor's side effects. It is well-established that the assembly of NLRP3 triggers an inflammatory response, leading to various diseases and highlighting its significance as a therapeutic target. Among the methods used in the discovery of new drugs, Computer-Aided Drug Desing (CADD) is widely used due to its numerous advantages, such as less financial investment and time of discovery, being critical, the ability to be used in any drug discovery campaign, including to search new antiinflammatory drugs targeting NLRP3. Finally, this review aims to present various computational methods, both traditional and current, that facilitate the rational design and discovery of new NLRP3 inflammasome inhibitors. This contributes to developing innovative anti-inflammatory drugs that may be used in future clinical applications. 

Information of the electronic structure origin of the photophysical properties is of paramount importance to understand the intricate physical/chemical transformations a molecule undergoes in the process of light absorption. Moreover, experimental analysis of excited states involved in the photophysical phenomenon is often difficult for their transiency, and hence quantum chemical information of the excited state emerges as the only tool for an in-depth understanding of the photoexcitation mechanism. Exploration of the ground (S0 ) and excited electronic states of molecules and subsequent estimation of absorption/emission wavelength need rigorous standardization of computational methodology. Hence, the chapter offers a general description of the state-of-the-art methodologies to explore the photophysical properties of the molecules, which are promising candidates for important applications. This bridging would ultimately aid in understanding the complex excited state phenomena occurring in different materials with much clarity fostering their development in varied verticals like medicine, biotechnology, energy, etc. Fluorescent active molecules and their subsequent structure-activity correlation would be the prime focus of the present piece thus rendering a suitable explanation of their excited state properties through theoretical modelling and explanation at the level of electronic structure. Application of the standardized methodology on a few chosen molecules of probable industrial importance such as the smallest known Green Fluorescent Protein (GFP), 3-hydroxy-4-pyridine carboxaldehyde (HINA), 2-hydroxy-3-naphthaldehyde semicarbazone (2H3NS), etc. would provide ample scope to validate the computational data through comparison with the already available experimental dataset. The theoretical interpretations of photo-responsiveness of future industrially important molecules through standardized computational methodology are likely to be a colossal accrue of the current book chapter.