The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

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, to assist 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, human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.Comment: 11 pages, 1 figur

Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Early assessment of the potential impact of chemicals on health and the environment requires toxicological properties of the molecules. Predictive modeling is often used to estimate the property values\ua0in silico\ua0from pre-existing experimental data, which is often scarce and uncertain. One of the ways to advance the predictive modeling procedure might be the use of knowledge existing in the field. Scientific publications contain a vast amount of knowledge. However, the amount of manual work required to process the enormous volumes of information gathered in scientific articles might hinder its utilization. This work explores the opportunity of semiautomated knowledge extraction from scientific papers and investigates a few potential ways of its use for predictive modeling. The knowledge extraction and predictive modeling are applied to the field of acute aquatic toxicity. Acute aquatic toxicity is an important parameter of the safety assessment of chemicals. The extensive amount of diverse information existing in the field makes acute aquatic toxicity an attractive area for investigation of knowledge use for predictive modeling. The work demonstrates that the knowledge collection and classification procedure could be useful in hybrid modeling studies concerning the model and predictor selection, addressing data gaps, and evaluation of models’ performance

Hybrid protein-polymer nanoparticles loaded with cisplatin: synthesis and characterization

Nowadays, many research related tovhybrid materials and the advances in Reversible-Deactivation Radical Polymerization (RDRP) techniques have enabled the development of responsive materials. These compounds respond to specific stimuli and have been integrating many research projects involving different drug delivery systems. In particular, hybrid conjugates based on protein−polymer have been integrating different formulations already approved by the Food and Drug Administration. In general, protein-polymer conjugates can increase the drug plasmatic half-life, altering the drug biodistribution profile and opening the possibility to reduce the dose administrated, which is a relevant advantage for patients. In this work, poly (N-vinylcaprolactam) (PNVCL) and poly (2-dimethylamino-ethyl methacrylate) (PDMAEMA) polymers were grafted to the surface of a protein model, the bovine serum albumin (BSA), by grafting-from approach, using the Atom Transfer Radical Polymerization (ATRP) technique. Firstly, a macroinitiator (BSA-MI) was successfully obtained and characterized by Sodium dodecyl sulfate polyacrylamide gel electrophoresis and Matrix-Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry by modifying lysine groups present in the BSA. Then, the BSA-PNVCLco-PDMAEMA hybrid was synthesized using BSA-MI as an initiator. The conjugate production was evaluated, revealing significant changes in the nanoparticles’ molecular mass and zeta potential . Additionally, it is demonstrated that altering the monomers' ratio can further adjust the lower critical solution temperature (LCST) of the protein-polymer conjugates. The results indicate the obtaining of a BSA-PNVCL-coPDMAEMA able to encapsulate approximately 1.9 mg of cisplatin for each 1 mg of the hybrid, making this conjugate a very promising hybrid material with desirable properties for a possible application in smart drug delivery systems