The Life of a Chemist in Biotech Living with Artificial Intelligence

This morning, I read this sentence from an article titled Future of AI will mean having a Ph.D. army in your pocket.

Imagine waking up and having your AI assistant brief you on the latest research, synthesize data, and plan your day — all while keeping tabs on opportunities in your field and scanning for personal or professional breakthroughs.

So I imagined this situation from my perspective. Like this.

The life of a chemist working in biotech can often be characterized by repetitive tasks. To an outsider, the daily activities of a chemist may seem monotonous and lacking in variety. While routines can differ from person to person, it is likely that medicinal chemists involved in drug discovery projects at biotech companies engage in the following common activities:

1. Task Review: Evaluating the priority of daily tasks based on the progress of ongoing projects.

2. Reading Papers: Searching for research papers relevant to their work, reviewing abstracts for pertinent information, and if necessary, reading the full text while taking notes on key findings.

3. Conducting Experiments: Performing organic synthesis experiments with various equipment, devices, and machines, meticulously recording all experimental details in a lab notebook.

4. Analyzing Results: Uploading and analyzing experimental results in a database for further evaluation.

5. Planning Experiments: Preparing for upcoming experiments based on previous analyses, ordering required reagents or supplies, and setting a timeline for future experiments based on delivery expectations.

6. Research Meetings: Creating presentations for research meetings to share experimental results, analysis findings, and future plans, as well as revising information based on feedback received.

Biotech researchers typically gain these skills during their graduate studies, allowing them to execute each task efficiently. This efficiency not only saves time but also fosters creativity, ultimately boosting the productivity of research projects.

Among the six activities mentioned, certain tasks could greatly benefit from advanced AI models:

• Task Review: AI can evaluate project progress from various sources (databases, emails, messaging apps, meeting notes) and organize tasks according to individual roles. While many people rely on personal to-do lists for this purpose, AI’s contextual understanding and organizational capabilities make it likely that it already outperforms humans in this area.

• Reading Papers: AI excels in searching for relevant papers, determining which abstracts warrant further reading, and extracting necessary information from full texts. Many chemists typically categorize their reading routines into three main approaches: checking an ASAP list for specific papers, using search engines like Google Scholar, or referencing other papers. Automating this process—albeit requiring continuous adjustments—could lead to receiving a well-organized list of relevant papers each morning. Current large language models (LLMs) are proficient at reading and summarizing text, making AI involvement in this task highly advantageous.

• Analyzing Results and Planning Experiments: This involves processing experimental data to extract important values and using statistical methods or visualization techniques to find correlations. Identifying correlations in data and developing predictive models are areas where AI excels; generative chemistry models can help plan future experiments as well.

• Research Meetings: These meetings often involve interpersonal dynamics—people gathering to discuss verbally. By focusing on work-related discussion points and action items, AI can significantly streamline these meetings if they are recorded and analyzed by an AI model. Recent advancements in AI provide substantial background information, enhancing its ability to perform this task effectively. Many commercial tools have already been developed for such purposes.

One area where AI may face challenges is in conducting experiments. While automation efforts have made strides in synthetic experiments, the range of chemical spaces that can be addressed through automation remains highly limited. Given the exploratory nature of chemical research, manual experimentation will likely remain a crucial component of the field for the foreseeable future.

When hiring new employees, there is a clear distinction between how quickly individuals can improve efficiency through repetitive tasks compared to the speed at which AI models can enhance efficiency. With the rapid advancement of AI technology, it is reasonable to assume that AI’s improvement rate will soon surpass that of human efficiency, though predicting the duration of this trend is complex. Human efficiency often involves inherent biases, while it is expected that AI will demonstrate comparatively fewer biases. As a result, many tasks will inevitably be entrusted to AI models in the long run. Although individuals may optimize specific tasks, overall workflow optimization will likely require AI assistance.

Ultimately, future laboratories will see most tasks performed by AI models—except those that truly require human intervention due to their unique versatility and efficiency. Humans will manage, refine, and enhance specific tasks while determining whether to accept changes based on their impact on final outcomes.

The pace at which new technologies are adopted varies among individuals, and only time will reveal the optimal moment for such adoption. However, many chemists working in biotech are already leveraging AI models in various ways—or at least envisioning potential applications.

Here are three key takeaways from the writing:

1. Routine Tasks and Efficiency: The daily life of a chemist in biotech largely involves repetitive tasks, such as task review, reading papers, conducting experiments, and analyzing results. The efficiency gained from these repetitive tasks allows chemists to allocate more time to creative thinking and enhances overall productivity in research projects.

2. AI Integration in Research Processes: Advanced AI models have the potential to significantly improve various aspects of a chemist’s work, including task organization, literature review, data analysis, and meeting management. By automating these processes, AI can streamline workflows and enhance the effectiveness of research efforts.

3. Human vs. AI Roles in Experimentation: While AI can optimize many tasks and workflows, certain activities, particularly hands-on experimentation, will likely remain reliant on human intervention due to the exploratory nature of chemical research. The collaboration between human chemists and AI is expected to evolve, with humans focusing on oversight and critical decision-making while AI handles routine processes.