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

Blanco-González, Alexandre; Cabezón, Alfonso; Seco-González, Alejandro; Conde-Torres, Daniel; Antelo-Riveiro, Paula; Piñeiro, Ángel; García‐Fandiño, Rebeca

doi:10.3390/ph16060891

Cited by 214 publications

(61 citation statements)

References 53 publications

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“…An example how this can be done appeared in a recent preprint in which the authors describe their use of ChatGPT (Blanco-Gonzalez et al, 2022). However, disclosure by mentioning this information only in the text also presents challenges, especially in terms of findability of articles that used LLMs, due to issues such as a lack of indexing (in the case of non-English content) and access to the full article text (in the case of paywalled content), or consistency of disclosures which could impact openness and transparency (e.g.…”

Section: Disclosing the Use Of Llms In The Body Of The Textmentioning

confidence: 99%

“…if some studies under report the use of LLMs). In Blanco-Gonzalez et al (2022: p.2), the authors highlighted the extent of their use (i.e. “total percentage of similarity between the preliminary text, obtained directly from ChatGPT, and the current version of the manuscript”) and added that 33.9% of the manuscript comprise of text generated by ChatGPT and is used verbatim or after revision (“identical 4.3%, minor changes 13.3% and related meaning 16.3%”).…”

Section: Disclosing the Use Of Llms In The Body Of The Textmentioning

confidence: 99%

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The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts

2023

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In this article, we discuss ethical issues related to using and disclosing artificial intelligence (AI) tools, such as ChatGPT and other systems based on large language models (LLMs), to write or edit scholarly manuscripts. Some journals, such as Science, have banned the use of LLMs because of the ethical problems they raise concerning responsible authorship. We argue that this is not a reasonable response to the moral conundrums created by the use of LLMs because bans are unenforceable and would encourage undisclosed use of LLMs. Furthermore, LLMs can be useful in writing, reviewing and editing text, and promote equity in science. Others have argued that LLMs should be mentioned in the acknowledgments since they do not meet all the authorship criteria. We argue that naming LLMs as authors or mentioning them in the acknowledgments are both inappropriate forms of recognition because LLMs do not have free will and therefore cannot be held morally or legally responsible for what they do. Tools in general, and software in particular, are usually cited in-text, followed by being mentioned in the references. We provide suggestions to improve APA Style for referencing ChatGPT to specifically indicate the contributor who used LLMs (because interactions are stored on personal user accounts), the used version and model (because the same version could use different language models and generate dissimilar responses, e.g., ChatGPT May 12 Version GPT3.5 or GPT4), and the time of usage (because LLMs evolve fast and generate dissimilar responses over time). We recommend that researchers who use LLMs: (1) disclose their use in the introduction or methods section to transparently describe details such as used prompts and note which parts of the text are affected, (2) use in-text citations and references (to recognize their used applications and improve findability and indexing), and (3) record and submit their relevant interactions with LLMs as supplementary material or appendices.

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Section: Disclosing the Use Of Llms In The Body Of The Textmentioning

confidence: 99%

Section: Disclosing the Use Of Llms In The Body Of The Textmentioning

confidence: 99%

The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts

2023

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“…Finally, because of its conversational interface, ChatGPT represents the greatest jump forward in user interpretability [13]. Nonetheless, Blanco Gonzalez et al [14] came to the conclusion that without significant human intervention, ChatGPT is inefficient in terms of producing trustworthy scientific texts. It lacks the knowledge and expertise to convey complex scientific concepts and information adequately and accurately.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic and Management Performance of ChatGPT in Obstetrics and Gynecology

Allahqoli¹,

Ghiasvand²,

Mazidimoradi³

et al. 2023

Gynecol Obstet Invest

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Objectives: The use of artificial intelligence (AI) in clinical patient management and medical education has been advancing over time. ChatGPT was developed and trained recently, using a large quantity of textual data from the internet. Medical science is expected to be transformed by its use. The present study was conducted to evaluate the diagnostic and management performance of the ChatGPT AI model in obstetrics and gynecology. Design: A cross-sectional study was conducted. Participants/Materials, Setting, Methods: This study was conducted in Iran in March 2023. Medical histories and examination results of 30 cases were determined in six areas of obstetrics and gynecology. The cases were presented to a gynecologist and ChatGPT for diagnosis and management. Answers from the gynecologist and ChatGPT were compared, and the diagnostic and management performance of ChatGPT were determined. Results: Ninety percent (27 of 30) of the cases in obstetrics and gynecology were correctly handled by ChatGPT. Its responses were eloquent, informed, and free of a significant number of errors or misinformation. Even when the answers provided by ChatGPT were incorrect, the responses contained a logical explanation about the case as well as information provided in the question stem. Limitations: The data used in this study were taken from the electronic book and may reflect bias in the diagnosis of ChatGPT. Conclusions: This is the first evaluation of ChatGPT’s performance in diagnosis and management in the field of obstetrics and gynecology. It appears that ChatGPT has potential applications in the practice of medicine and is (currently) free and simple to use. However, several ethical considerations and limitations such as bias, validity, copyright infringement, and plagiarism need to be addressed in future studies.

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“…These techniques often rely on the evaluation of huge data sets that can be used in a variety of ways (Gómez-Bombarelli et al, 2018). For instance, these methods are more accurate and effective than conventional ones at predicting the efficacy and toxicity of novel therapeutic molecules (Blanco-González et al, 2023). Additionally, AI-based algorithms can be used to find new medication targets, such as certain proteins or genetic pathways linked to disease (You et al, 2019).…”

Section: Limitations Of Current Ai Methods In Drug Discoverymentioning

confidence: 99%

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Agu,

Obulose

2024

Drug Development Research

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The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well‐known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI‐based models for drug–target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.

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

Cited by 214 publications

References 53 publications

The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts

The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts

Diagnostic and Management Performance of ChatGPT in Obstetrics and Gynecology

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

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