TL;DR
The author revisits three pieces from 2024 on generative chemistry, molecular dynamics, and wet‑lab innovation, updating readers on what has changed. For the generative chemistry piece he says synthesis remains a constraint, but greater investment and an expanded pool of 'easily synthesizable' molecules (from ~40B to ~80B) have weakened that bottleneck.
What happened
In this January 2026 essay the writer re-examines three articles published in 2024: a September piece arguing that generative ML in small‑molecule chemistry was limited by synthesis, a June post on the importance of molecular dynamics data for protein models, and a July article asserting that wet‑lab advances would drive AI in biology. The author summarizes each original thesis, surveys developments since, and issues verdicts. For the generative‑chemistry argument he reports that arbitrary synthesis remains difficult and ML models are not yet reliable at producing practical synthesis routes, but more capital has flowed into synthesis capabilities and the set of easily synthesizable molecules has reportedly grown from roughly 40 billion to about 80 billion. He notes pushback from online communities on stating the obvious, and describes his method of revisiting theses rather than quietly abandoning predictions. Details on follow‑up outcomes for the molecular dynamics and wet‑lab pieces are not provided in the source.
Why it matters
- Chemical synthesis constraints can limit the real‑world impact of generative molecular models.
- An expanding catalog of readily synthesizable molecules reduces the practical gap between in‑silico design and lab production.
- Increased funding into synthesis infrastructure may accelerate adoption of computational design tools.
- Clarifying where ML models still fail (e.g., proposing usable synthesis routes) focuses research and commercial priorities.
Key facts
- Author: Abhishaike Mahajan; revisit published Jan 07, 2026.
- Three original 2024 posts revisited: Sept 16 (generative ML in chemistry), June 2 (molecular dynamics for protein ML), July 15 (wet‑lab innovations and AI).
- On generative chemistry, author’s tl;dr: synthesis remains hard and models are imperfect at route prediction.
- Author reports the set of 'easily synthesizable' molecules grew from ~40 billion to ~80 billion.
- More investment has flowed into improving synthesis capabilities since 2024, according to the author.
- The author encountered critique on r/chemistry for the initial 2024 synthesis argument.
- While in San Francisco, the author met researchers John Bradshaw and Gina El Nesr; he also plans to co‑host an event with Tamarind Bio on Jan 16 at Southern Pacific Brewing (confirmed in the source).
- The piece is published on a paid blog that offers a 7‑day free trial to read the full post.
What to watch next
- Further expansion of the catalog of 'easily synthesizable' molecules beyond ~80B (not confirmed in the source).
- Progress of ML models that propose practical synthesis routes and paired lab validation (not confirmed in the source).
- Adoption of molecular dynamics datasets by next‑generation protein ML models — degree and timeline not confirmed in the source.
Quick glossary
- Generative ML: Machine learning methods that create new data samples, such as novel chemical structures or biological sequences, based on patterns learned from training data.
- Chemical synthesis: The set of laboratory processes and reactions used to create chemical compounds from precursor materials.
- Molecular dynamics: A computational technique that simulates the physical movements of atoms and molecules over time to study structure and behavior.
- Wet lab: A laboratory space where biological or chemical experiments are performed using liquids, reagents, and physical apparatus rather than purely computational work.
Reader FAQ
Was the author right that generative chemistry is bottlenecked by synthesis?
He says yes in a qualified way: synthesis remains difficult and models don't yet provide reliable route plans, though the practical synthesizable space has grown.
How large did the author say the synthesizable molecule set became?
He reports it expanded from about 40 billion to roughly 80 billion.
Does the source provide outcomes for the molecular dynamics and wet‑lab predictions?
Not confirmed in the source.
Is the full revisit article free to read?
The post is on a paid blog; the site offers a 7‑day free trial according to the source.
MISC A 2026 look at three bio-ML opinions I had in 2024 6.6k words, 30 minutes reading time ABHISHAIKE MAHAJAN JAN 07, 2026 ∙ PAID 13 1 1 Share Note:…
Sources
- A 2026 look at three bio-ML opinions I had in 2024
- Chaos is a Ladder: Predictions for 2026 – LifeSciVCLifeSciVC
- My 4 AI Predictions for 2026
- A Look Ahead: Predictions for the Life Sciences in 2026
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