Researchers have highlighted concerns regarding hallucinations in LLM due to their generation of plausible but inaccurate or unrelated content. However, these hallucinations have potential in the fields driven by creativity as the discovery of drugs, where innovation is essential. LLMs have been widely applied to scientific domains, such as the science of materials, biology and chemistry, helping tasks such as molecular description and drug design. While traditional models such as MOLT5 offer specific domain precision, LLMs often produce hallucinated exits when they do not fit. Despite its lack of objective consistency, such results can provide valuable information, such as high -level molecular descriptions and potential compound applications, which supports exploratory processes in the discovery of drugs.
The discovery of drugs, an expensive and intensive process in time, implies evaluating vast chemical spaces and identifying new solutions to biological challenges. Previous studies have used automatic learning and generative models to help in this field, with researchers that explore the integration of LLM for the design of molecules, the healing of the data set and prediction tasks. The hallucinations in LLMS, often seen as an inconvenience, can imitate creative processes recombing knowledge to generate novel ideas. This perspective is aligned with the role of creativity in innovation, exemplified by innovative accidental discoveries such as penicillin. By taking advantage of hallucinated ideas, LLM could advance in the discovery of drugs identifying molecules with unique properties and promoting high -level innovation.
Researchers at the Technological University of SCADS.ai and DresDE propose the hypothesis that hallucinations can improve LLM's performance in drug discovery. Using seven LLMS adjusted by instructions, including GPT-4O and call-3.1-8b, incorporated hallucinated natural language descriptions of the molecules smile chains in the indications for classification tasks. The results confirmed their hypothesis, with call-3.1-8b achieving a Roc-Auc improvement of 18.35% on the baseline. The largest models and the hallucinations generated by the Chinese demonstrated the greatest profits. The analyzes revealed that the hallucinated text provides unrelated but insightful information, helping predictions. This study highlights the potential of hallucinations in pharmaceutical research and offers new perspectives on the use of LLMs for innovative drug discovery.
To generate hallucinations, the strings of the smile molecules are translated into natural language using a standardized notice where the system is defined as an “drug discovery expert.” The descriptions generated are evaluated for objective consistency using the HHM-2.1 model opens, with text generated by MOLT5 as a reference. The results show a low factual consistency between LLMS, with Chemllm with 20.89% score and others with an average of 7.42–13.58%. Drug discovery tasks are formulated as binary classification problems, predicting specific molecular properties through the prediction of the next Token. The indications include smiles, descriptions and task instructions, with limited models to the output “yes” or “no” according to the greatest probability.
The study examines how the hallucinations generated by different LLM have the impact performance on the prediction of molecular property. The experiments use a standardized immediate format to compare predictions based on solo smile chains, smiles with descriptions generated by MOLT5 and hallucinated descriptions of several LLM. Five Moleculenet data sets were analyzed using ROC-AUC scores. The results show that hallucinations generally improve performance on smiles or Molt5 baselines, with GPT-4O achieving the highest profits. The largest models benefit more from hallucinations, but more than 8 billion parameters are stabilized. Temperature configuration influences hallucination quality, with intermediate values that produce the best performance improvements.
In conclusion, the study explores the potential benefits of hallucinations in LLM for drug discovery tasks. By hypothesizing that hallucinations can improve performance, research evaluates seven LLM in five data sets using descriptions of hallucinated molecules integrated into indications. The results confirm that hallucinations improve LLM performance compared to reference indications without hallucinations. In particular, call-3.1-8b achieved a Roc-Auc gain of 18.35%. The hallucinations generated by GPT-4O provided consistent improvements between the models. The results reveal that larger model sizes generally benefit more from hallucinations, while factors such as generation temperature have a minimum impact. The study highlights the creative potential of hallucinations in ai and encourages a greater exploration of drug discovery applications.
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Sana Hassan, a consulting intern in Marktechpost and double grade student in Iit Madras, passionate to apply technology and ai to address real world challenges. With great interest in solving practical problems, it provides a new perspective to the intersection of ai and real -life solutions.