Recent Advances in Chemical Artificial Intelligence

Abstract submission deadline

15 July 2026

Manuscript submission deadline

15 October 2026

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Topic Information

Dear Colleagues,

Artificial Intelligence (AI) and robotics are becoming more prevalent in our societies. They assist, and sometimes even replace, humans in accomplishing specific tasks and dealing with complex systems. Traditionally, AI and robotics are developed in software and hardware, the latter being either rigid or soft. In the early 2010s, a new promising strategy was put forward as follows: to use molecular, supramolecular, and systems chemistry in wetware (i.e., in fluid solutions) to mimic some performances of human (and, more generally, biological) intelligence and develop the so-called Chemical Artificial Intelligence (CAI) and Chemical Robotics. In CAI, information is encoded, collected, stored, processed, and sent primarily through molecules and chemical reactions. CAI and Chemical Robots will allow humans to colonize the microscopic world. Microscopic artificial intelligent chemical systems will help in diagnosing and curing diseases, safeguarding the environment, and contributing to increased energy and food supplies. Finally, designing and implementing various forms of autonomous chemical intelligence will allow for a deeper comprehension of two remarkable emergent properties: intelligence and life.

This topic accepts research articles, reviews, and perspectives presenting the burgeoning field of CAI and Chemical Robotics, with the ambition of gathering brilliant ideas of the principal investigators in this new and forward-thinking research field and promoting the generation of a research network for further scientific collaborations.

Prof. Dr. Pier Luigi Gentili
Prof. Dr. Jerzy Górecki
Prof. Dr. David C Magri
Prof. Pasquale Stano
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Molecules molecules	4.6	8.6	1996	15.1 Days	CHF 2700	Submit
Biomimetics biomimetics	3.9	4.2	2016	17 Days	CHF 2200	Submit
Chemosensors chemosensors	3.7	7.3	2013	19.1 Days	CHF 2000	Submit
Life life	3.4	6.0	2011	16.6 Days	CHF 2600	Submit
AI ai	5.0	6.9	2020	19.2 Days	CHF 1800	Submit
Sci sci	-	5.2	2019	26.7 Days	CHF 1400	Submit

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Published Papers (3 papers)

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All Molecules Biomimetics Chemosensors Life AI Sci

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21 pages, 3861 KB  

Open AccessArticle

Development of QSAR Models and Web Applications for Predicting hDHFR Inhibitor Bioactivity Using Machine Learning

by Ibrahim Maattallaoui, Mahamadou Sakho, Abdellah Maatallaoui, Enrique Barrajón-Catalán and Noureddine El Aouad

Molecules 2025, 30(23), 4618; https://doi.org/10.3390/molecules30234618 - 1 Dec 2025

Viewed by 1084

Abstract

Human dihydrofolate reductase (hDHFR) is a crucial cellular enzyme in folate metabolic pathway, where it catalyzes the reduction of dihydrofolate into tetrahydrofolate (THF) and an important cofactor involved in DNA, RNA, protein biosynthesis and cell proliferation. Due to its importance, hDHFR has become [...] Read more.

Human dihydrofolate reductase (hDHFR) is a crucial cellular enzyme in folate metabolic pathway, where it catalyzes the reduction of dihydrofolate into tetrahydrofolate (THF) and an important cofactor involved in DNA, RNA, protein biosynthesis and cell proliferation. Due to its importance, hDHFR has become a promising target for therapeutic development, particularly in treating cancer, bacterial infections, and autoimmune diseases. Its inhibition has found clinical value in antitumor, antimicrobial and antiprotozoal treatment; however, the emergence of resistance to existing hDHFR inhibitors necessitates the development of new and more potent compounds. In the current study, we propose a cheminformatics-based approach using machine learning to develop predictive models of hDHFR bioactivity. We used three types of molecular descriptors in the form of fingerprints, i.e., PubChem, Substructure, and MACCS, to capture structural properties associated with hDHFR inhibition. Predictive models were built using a random forest algorithm optimized through hyperparameter tuning. Feature selection was performed using Recursive Feature Elimination (RFE), and dataset dimensionality was reduced by removing outliers through Principal Component Analysis (PCA) to optimize model performance and reducing overfitting and weak predictivity. The resulting models are validated through external test sets, domain applicability analysis, and interpretation of influential molecular features via random forest feature importance selection plots and correlation matrix analysis. All three models exhibited strong predictive capabilities, with R-squared (R²) values ranging from 0.9849 to 0.9934 for the training set and 0.9381 to 0.9591 for the test set. These final predictive models were further incorporated into an accessible web application, enabling users to estimate the bioactivity of new compounds targeting hDHFR. Full article

(This article belongs to the Topic Recent Advances in Chemical Artificial Intelligence)

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23 pages, 752 KB  

Open AccessPerspective

Quantum Artificial Intelligence: Some Strategies and Perspectives

by Marco Baioletti, Fabrizio Fagiolo, Corrado Loglisci, Vito Nicola Losavio, Angelo Oddi, Riccardo Rasconi and Pier Luigi Gentili

AI 2025, 6(8), 175; https://doi.org/10.3390/ai6080175 - 1 Aug 2025

Cited by 2 | Viewed by 4901

Abstract

In the twenty-first century, humanity is compelled to face global challenges. Such challenges involve complex systems. However, science has some cognitive and predictive limits in dealing with complex systems. Some of these limits are related to computational complexity and the recognition of variable [...] Read more.

In the twenty-first century, humanity is compelled to face global challenges. Such challenges involve complex systems. However, science has some cognitive and predictive limits in dealing with complex systems. Some of these limits are related to computational complexity and the recognition of variable patterns. To overcome these limits, artificial intelligence (AI) and quantum computing (QC) appear to be helpful. Even more promising is quantum AI (QAI), which emerged from the combination of AI and QC. The combination of AI and QC produces reciprocal, synergistic effects. This work describes some of these effects. It shows that QC offers new materials for implementing AI and innovative algorithms for solving optimisation problems and enhancing machine learning algorithms. Additionally, it demonstrates how AI algorithms can help overcome many of the experimental challenges associated with implementing QC. It also outlines several perspectives for the future development of quantum artificial intelligence. Full article

(This article belongs to the Topic Recent Advances in Chemical Artificial Intelligence)

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24 pages, 1503 KB  

Open AccessArticle

The Effects of Omeprazole on the Neuron-like Spiking of the Electrical Potential of Proteinoid Microspheres

by Panagiotis Mougkogiannis and Andrew Adamatzky

Molecules 2024, 29(19), 4700; https://doi.org/10.3390/molecules29194700 - 4 Oct 2024

Cited by 3 | Viewed by 2033

Abstract

This study examines a new approach to hybrid neuromorphic devices by studying the impact of omeprazole–proteinoid complexes on Izhikevich neuron models. We investigate the influence of these metabolic structures on five specific patterns of neuronal firing: accommodation, chattering, triggered spiking, phasic spiking, and [...] Read more.

This study examines a new approach to hybrid neuromorphic devices by studying the impact of omeprazole–proteinoid complexes on Izhikevich neuron models. We investigate the influence of these metabolic structures on five specific patterns of neuronal firing: accommodation, chattering, triggered spiking, phasic spiking, and tonic spiking. By combining omeprazole, a proton pump inhibitor, with proteinoids, we create a unique substrate that interfaces with neuromorphic models. The Izhikevich neuron model is used because it is computationally efficient and can accurately simulate the various behaviours of cortical neurons. The results of our simulations show that omeprazole–proteinoid complexes have the ability to affect neuronal dynamics in different ways. This suggests that they could be used as adjustable components in bio-inspired computer systems. We noticed a notable alteration in the frequency of spikes, patterns of bursts, and rates of adaptation, especially in chattering and triggered spiking behaviours. The findings indicate that omeprazole–proteinoid complexes have the potential to serve as adaptable elements in neuromorphic systems, presenting novel opportunities for information processing and computation that have origins in neurobiological principles. This study makes a valuable contribution to the expanding field of biochemical neuromorphic devices and establishes a basis for the development of hybrid bio-synthetic computational systems. Full article

(This article belongs to the Topic Recent Advances in Chemical Artificial Intelligence)

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