Natural and Synthetic Compounds: Processes, Challenges, and Opportunities for Human Health and Environmental Sustainability

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Pharmaceutical Processes".

Deadline for manuscript submissions: closed (15 October 2025) | Viewed by 1264

Special Issue Editors


E-Mail Website1 Website2
Guest Editor
Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, Romanian Academy, 24 Mihai Viteazu Ave., 300223 Timisoara, Romania
Interests: computational chemistry; QSAR; drug discovery; molecular simulation; medicinal chemistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Associations between natural and synthetic compounds are essential in improving human health and supporting environmental sustainability. Nature is a valuable source when it comes to developing and designing lead compounds for drugs that could significantly improve treatments for serious diseases, enhancing general health and wellbeing. Herbal systems are essential in healthcare because they provide natural sources for approximately 60% of chemotherapeutic agents. The emergence of cutting-edge technologies and methods to improve the drug design process has provided new ways of processing complex natural products, using their structures to develop new and innovative drug candidates with minimized side effects. Ensuring sustainability in research on natural and synthetic compounds is fundamental in adopting eco-friendly approaches to human health and environmental sustainability.

In this Special Issue of Processes, we will summarize current trends in drug discovery and development, while also reporting the latest applications for natural and synthetic compounds to combat current and future health challenges, as well as strategies and computational approaches that facilitate the design of new and innovative drugs with greater accuracy, low costs, and high efficiency. Furthermore, this Special Issue will explore the connections between natural and synthetic compounds, highlighting their individual or combined potential to provide therapeutic and environmental sustainability benefits.

This Special Issue welcomes original research papers, communications, reviews, and other articles reporting recent applications for both natural and synthetic compounds in drug discovery and development, employing advanced methods and tool and new protocols, targets, sustainable strategies, and innovative technologies for a sustainable future, with a key role in next-generation drug discovery. These advances will not only improve efficiency and effectiveness in the design of new therapeutic agents but will also ensure that the processes involved are environmentally friendly and sustainable.

Dr. Alina Bora
Dr. Luminita Crisan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • natural compounds
  • bioactive compounds
  • computational methods
  • drug discovery and development
  • drug repurposing
  • green chemistry
  • biological testing
  • sustainable strategies
  • healthy and sustainable challenges
  • natural and synthetic product application
  • medicinal chemistry

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

27 pages, 13228 KB  
Article
A Hybrid Machine Learning Pipeline for Reliable Prediction of Potential HIV-1 Inhibitors
by Ciprian-Bogdan Chirila, Lucia Gradinaru and Luminita Crisan
Processes 2025, 13(10), 3327; https://doi.org/10.3390/pr13103327 - 17 Oct 2025
Viewed by 388
Abstract
The discovery of potent antiviral inhibitors remains a major challenge in combating viral infections. In this study, we present a hybrid computational pipeline that integrates machine learning for accurate prediction of small-molecule HIV-1 inhibitors. Five classification algorithms were trained on 7552 known inhibitors [...] Read more.
The discovery of potent antiviral inhibitors remains a major challenge in combating viral infections. In this study, we present a hybrid computational pipeline that integrates machine learning for accurate prediction of small-molecule HIV-1 inhibitors. Five classification algorithms were trained on 7552 known inhibitors from ChEMBL using five classes of molecular fingerprints. Among these, Random Forest (RFC) models consistently outperformed the others, achieving accuracy values of 0.9526 to 0.9932, while K-Nearest Neighbors (KNN) and Multilayer Perceptron (MLP) models, although slightly less accurate, still demonstrated robust performance, with accuracies ranging from 0.9170 to 0.9482 and 0.9071 to 0.9179 for selected descriptors, respectively. Based on model predictions, 4511 natural compounds from the COCONUT database were identified as potential inhibitors. After 3D shape similarity filtering (Tanimoto Combo > 1 and Shape Tanimoto > 0.8), eight top-ranked compounds were prioritized for further assessment of their physicochemical, ADMET, and drug-likeness properties. Two natural compounds, CNP0194477 and CNP0393067, were identified as the most promising candidates, showing low cardiotoxicity (hERG risk: 0.096 and 0.112), favorable hepatotoxicity and genotoxicity profiles, and good predicted oral absorption. This integrated workflow provides a robust and efficient computational strategy for the identification of natural compounds with antiviral potential, facilitating the selection of promising HIV-1 inhibitors for further experimental validation. Full article
Show Figures

Figure 1

Back to TopTop