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

Towards the total synthesis of (-)-phorbol for novel anti-cancer studies

Phorbol esters belong to the family of tigliane diterpenes isolated from Croton tiglium, a member of the Euphorbiaceae and Thymelaeaceae plants. Since its isolation in 1935 phorbol has had a rapid rise to fame as a tumor promoter via protein kinase C activation and is now a common tool for cancer biologists worldwide. The issue with investigating and evaluating phorbol and its derivatives, in a biological sense, is that these complex molecules are limited only to natural sources (i.e. isolation from plant material). Furthermore, in addition to the supply issue these compounds are only available in one enantiomeric series (i.e. the natural (+)-phorbol), which in combination with that mentioned above places extensive limits on any meaningful biological investigation. The non-natural isomer (-)-phorbol is not known, has never been synthesised and as such never been biologically evaluated. Considering the importance of phorbol to current cancer biologists, investigating the synthesis of the non-natural isomer and its derivatives is of great importance. This thesis describes fundamental studies towards the total synthesis of (-)-phorbol. The key step in the approach towards the total synthesis is a rhodium catalysed [4+3] cycloaddition reaction between a furan (right hand fragment) and a diazo compound (left hand fragment). The diazo compound was successfully synthesised from commercially available cyclopentenone over four steps via a palladium mediated direct diazo coupling protocol. In addition, the assembly of the CD-ring system en route to the right hand fragment is presented. Difficulties were encountered in the introduction of the D-ring system via cyclopropanation. To circumvent this problem it was discovered that a change of protecting group was essential for the success of this reaction. Moreover, a model study of the proposed [4+3] cycloaddition reaction between the left hand fragment and a number of furans of various substitution patterns is described. Furthermore, 1,2,3-sulfonyl triazoles are presented as suitable diazo substitutes as they undergo a “ring to chain isomerism’ to diazoimines. This equilibrium is being used in a novel approach to substituted cycloheptadiene systems with furans. In a rare case a [3+2] cycloaddition reaction was observed and a discussion of mechanistic insight is presented