Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges, and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research, are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, in terms of assisting human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, the human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and the scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.

Alkyne tethered benzamides undergo rhodium(III)-catalyzed intramolecular annulations to give tricyclic isoquinoline derivatives in good yields. DFT calculations suggest that the reaction mechanism involves a migratory insertion of the alkyne into the rhodium–nitrogen bond of the rhodacycle intermediate that results from the initial C–H activation. This contrasts with the pathway proposed for intermolecular cases, which considers an insertion into the rhodium–carbon instead of the rhodium–nitrogen bond. The annulation is also effective with acrylamides; and, while anilides fail to participate in the process, naphthylamides do undergo the intramolecular annulation, albeit the chemoselectivity is different than for the intermolecular reactions.

2-Alkenylphenols react with allenes upon treatment with catalytic amounts of Pd(II) and Cu(II), to give benzoxepine products, in high yields, and with very good regio- and diastereoselectivities. This contrasts with the results obtained with Rh catalysts, which provided chromene-like products through a pathway involving a β-hydrogen elimination step. Computational studies suggest that the square planar geometry of the palladium is critical to favor the reductive elimination process required for the formation of the oxepine products.

In this study, we describe the self-assembling properties of cyclic peptides containing γ-amino acids in lipid bilayers to form transmembrane nanotubes. The resulting ion channel models are selective for alkaline ions. Although the transport rates conform to the lyotropic series, these partially hydrophobic channels show an unexpectedly higher rate for sodium ions.

Biomimetic nanopores based on membrane-spanning single-walled carbon nanotubes have been designed to include selectivity filters based on combinations of anionic and cationic groups mimicking those present in bacterial porins and in voltage-gated sodium and calcium channels. The ion permeation and selectivity properties of these nanopores when embedded in a phospholipid bilayer have been explored by molecular dynamics simulations and free energy profile calculations. The interactions of the nanopores with sodium, potassium, calcium, and chloride ions have been explored as a function of the number of anionic and cationic groups within the selectivity filter. Unbiased molecular dynamics simulations show that the overall selectivity is largely determined by the net charge of the filter. Analysis of distribution functions reveals considerable structuring of the distribution of ions and water within the nanopores. The distributions of ions along the pore axis reveal local selectivity for cations around filter, even in those nanopores (C0) where the net filter charge is zero. Single ion free energy profiles also reveal clear evidence for cation selectivity, even in the C0 nanopores. Detailed analysis of the interactions of the C0 nanopore with Ca ²⁺ ions reveals that local interactions with the anionic (carboxylate) groups of the selectivity filter lead to (partial) replacement of solvating water as the ion passes through the pore. These studies suggest that a computational biomimetic approach can be used to evaluate our understanding of the design principles of nanopores and channels.