The integration of computational approaches into phytochemical and pharmacological research is revolutionizing drug discovery, therapeutic development, and drug formulation optimization. Techniques such as molecular docking, virtual screening, artificial intelligence (AI)-driven drug design, and machine learning-based predictive modeling are accelerating the identification of bioactive compounds from natural sources. These computational methods enable researchers to conduct precise structure-activity relationship (SAR) analyses, which are essential for understanding how molecular features contribute to bioactivity. Virtual screening allows for the rapid evaluation of large compound libraries, identifying potential lead candidates that could be overlooked by traditional experimental methods. AI and machine learning algorithms further refine predictive models, improving lead optimization and suggesting modifications to enhance therapeutic efficacy. Beyond identifying bioactive compounds, computational tools are pivotal in assessing the pharmacokinetics and toxicity of these compounds through in silico simulations, enabling predictions of absorption, distribution, metabolism, excretion (ADME), and potential toxicological effects. This early-stage assessment significantly reduces the need for expensive and time-consuming in vitro and in vivo experiments, allowing researchers to prioritize compounds with higher chances of success. However, it is crucial to emphasize that computational predictions must be complemented by rigorous in vitro and in vivo experiments and clinical trials to evaluate the tentative findings in pharmacological models. The integration of computational and experimental approaches may facilitate a more reliable and translational drug discovery process. This combined strategy minimizes false positives from virtual screenings and strengthens the pharmacological validity of identified lead compounds. Moreover, experimental assessment is essential to understand the impact on complex biological processes which computational models alone cannot capture, ensuring a pharmacologically relevant understanding of therapeutic potential. Without integration of in vitro or in vivo pharmacological approaches in silico approaches are mostly misleading.

The research aims to explore and further develop these advanced computational and pharmacological studies, facilitating the discovery of novel therapeutics and more efficient drug formulations for a broad spectrum of diseases. By enhancing the speed and accuracy of drug discovery, these methods are transforming how natural products are analyzed and utilized in therapeutic contexts, offering new avenues for drug design and improving patient outcomes. The growing reliance on integrated computational approaches not only accelerates the identification and optimization of bioactive compounds but also contributes to more cost-effective, efficient, and targeted drug development processes.

Natural products science is a broad field that comprises several sciences from Ethnobotany to formulation development, and there exists a great need to arrange all in a simplified and efficient mode. The current era of advanced computational and pharmacological techniques can revolutionize the discovery and optimization of bioactive compounds from natural sources. By addressing current research gaps, we aim to accelerate drug development and improve therapeutic outcomes through more efficient and targeted approaches. The goal of this Research Topic is to highlight the latest advancements in computational techniques such as molecular docking, virtual screening, AI-driven drug design, and machine learning-based modeling, and their integration with pharmacological assessments using in vitro and In vivo approaches However, most of the claims currently made based on docking studies are far to preliminary and often implausible, for example if conducted with polyphenols. Experimental approaches, especially animal studies and preliminary clinical trials, need to be integrated. Studies on computational simulations in optimizing drug formulations, predicting pharmacokinetic properties, and assessing toxicity will also be considered

The scope of this research topic includes the application and advancement of computational techniques in the natural products discovery, optimization, and biological evaluation of bioactive compounds.
Contributors are invited to address the following specific themes:

• Computational combined with in vitro or in vivo pharmacological investigations focusing on methods in drug discovery: Exploring the use of molecular docking, virtual screening, and AI-driven drug design for identifying and optimizing bioactive compounds from natural products.
• Integration of Computational and Experimental Approaches: Methods to combine computational simulations with experimental evaluation, enhancing the efficiency and accuracy of drug discovery processes.

• Optimised drug formulations: The role of computational techniques in optimizing drug formulations, including predicting molecular interactions that affect bioavailability, solubility, and stability.

• AI and machine learning in lead compound optimization: The application of machine learning-based predictive models for refining lead compounds, enhancing therapeutic efficacy, and reducing development costs.

• Computational drug design for personalized medicine: Exploration of how computational approaches can be used to develop personalized therapeutic strategies based on the molecular profile of diseases.

• Pharmacology of natural products and formulation design: Emphasis on underexplored natural products as sources of novel therapeutics, and how computational approaches can accelerate the discovery of these bioactive compounds.

• Future directions in computational Pytopharmacology: Discussion of emerging trends and future research opportunities in integration of computational approaches to phytochemical and pharmacological studies, with a focus on advancing the field towards more efficient and cost-effective drug development.

Important Note:

All contributions to this Research Topic must follow the guideline listed in this section:

• Purely in silico/AI-based studies are outside of our scope.

• The introduction needs to describe the background of the research object focusing on the traditional or local use of a traditional medicine and provide bibliographical references that illustrate its recent application in general healthcare.

• Network studies must critically assess the pharmacological evidence to evaluate the potential effects of a preparation / herbal (medical) product and the limitations of the evidence. An in vitro or in vivo assessment needs to be an integrated part of the study

• Chemical anti-oxidant assays like the DPPH or ABTS assay are of no pharmacological relevance, Therefore they can only be used a chemical-analytical assays without pharmacological claims.

• You need to self-assess your MS using the ConPhyMP tool (https://ga-online.org/best-practice) and submit the relevant sections of the tool with your submission and follow the standards established in the ConPhyMP statement Front. Pharmacol. 13:953205).

• All the manuscripts need to fully comply with the Four Pillars of Best Practice in Ethnopharmacology (you can freely download the full version here). Importantly, please ascertain that the ethnopharmacological context is clearly described (pillar 3d) and that the material investigated is characterized in detail (pillars 2 a and b).

Are you interested in contributing a manuscript to this Research Topic?

Computational Approaches in Pharmacological Research and Drug Development: How to Overcome the Challenges of Complex Matrices in Ethnopharmacology, Therapeutics, and Formulation Design