The innocent idea that became a marathon
The original plan seemed harmless enough. I thought it would be simple to write a concise review on the use of artificial intelligence in drug discovery, summarize a few recent advancements, and stitch together a readable narrative. It sounded entirely manageable during the first coffee-fueled discussion. What followed instead was an eight-month intellectual marathon filled with both fascination and fatigue.
The field of AI in drug discovery is so broad that every attempt to narrow it immediately splits into new branches. Each subdomain seems to contain ten more waiting underneath. Rather than trimming the scope like sensible people might, we made the bold, slightly naive decision to cover as much of the landscape as possible. It was equal parts ambition and curiosity, and it turned the project into an unexpectedly deep exploration.
When the search engine became a tidal wave
The first literature search for “AI” and “drug discovery” returned more than fifty thousand results. That number was less exciting than it was paralyzing. Several rounds of refinement, filtering, and quiet despair eventually reduced the pile to about five hundred key papers. Each one needed to be read, understood, and integrated into something coherent.
Organization soon became a survival mechanism. A sprawling spreadsheet became the backbone of the process, with every entry carefully tagged by application area, algorithm, and relevance. Semantic Scholar and Elicit made it possible to triage the papers efficiently, while Research Rabbit provided an oddly addictive visual representation of citations and relationships between studies.
The strange comfort of digital clutter
My workspace was a reflection of two opposite instincts: structure and chaos. EndNote handled references with precision worthy of an accountant, while Obsidian became a sandbox of half-formed notes, scattered insights, and hastily saved links that I repeatedly promised myself to clean up. Somewhere between the two, the outline of the paper began to take shape.
One realization became increasingly clear as the pile of reading grew taller: some of the most original and technically elegant work does not appear in high-impact journals. Many outstanding contributions first appear on arXiv, quietly building influence long before formal publication. It was a reminder that great science often thrives outside the traditional pathways of prestige.
Finding the narrative
Writing a review is never just about summarizing information. It is about uncovering the relationships between ideas and showing how they evolve. In this field, the connecting threads wove through every major phase of the drug discovery pipeline: from target identification to hit discovery, from lead optimization to ADMET prediction. The literature seemed fragmented at first, but as I traced the conceptual bridges, a narrative began to emerge about how these methods were quietly converging toward a unified ecosystem of data-driven discovery.
The quiet victories
There were no grand moments of revelation here, only a collection of small, patient wins. A paragraph rewritten for clarity. A figure that finally made sense after the tenth draft. A table that no longer felt like a tax audit. Gradually, chaos gave way to rhythm, and the project began to resemble something worth reading.
Looking back, the process was equal parts exhausting and rewarding. It reminded me that science is not only about discovery but also about the architecture of understanding itself. Writing forced me to connect scattered fragments into something continuous, and in doing so, I came to appreciate the quiet discipline that underlies every meaningful piece of scientific communication.