I was taken aback by a recent CNBC article entitled “ Generative AI will be designing new drugs all on its own in the near future ”. I should know better than to pay attention to AI articles in the popular press, but I feel that even scientists working in drug discovery may have a skewed perception of what generative AI can and can’t do. To understand exactly what’s involved, it might be instructive to walk through a typical generative molecular design workflow and point out a few things. First, these programs are far from autonomous. Even when presented with a well-defined problem, generative algorithms produce a tremendous amount of nonsense. Second, domain expertise is essential when sifting through the molecules produced by a generative algorithm. Without a significant medicinal chemistry background, one can’t make sense of the results. Third, while a few nuggets exist in the generative modeling output, a lot of work and good old-fashioned c...
Most papers describing new methods for machine learning (ML) in drug discovery report some sort of benchmark comparing their algorithm and/or molecular representation with the current state of the art. In the past, I’ve written extensively about statistics and how methods should be compared . In this post, I’d like to focus instead on the datasets we use to benchmark and compare methods. Many papers I’ve read recently use the MoleculeNet dataset, released by the Pande group at Stanford in 2017, as the “standard” benchmark. This is a mistake. In this post, I’d like to use the MoleculeNet dataset to point out flaws in several widely used benchmarks. Beyond this, I’d like to propose some alternate strategies that could be used to improve benchmarking efforts and help the field to move forward. To begin, let’s examine the MoleculeNet benchmark, which to date, has been cited more than 1,800 times. The MoleculeNet collection consis...
“Pay no attention to the man behind the curtain” - The Wizard of Oz Introduction Recently, a few groups have proposed general-purpose large language models (LLMs) like ChatGPT , Claude , and Gemini as tools for generating molecules. This idea is appealing because it doesn't require specialized software or domain-specific model training. One can provide the LLM with a relatively simple prompt like the one below, and it will respond with a list of SMILES strings. You are a skilled medicinal chemist. Generate SMILES strings for 100 analogs of the molecule represented by the SMILES CCOC(=O)N1CCC(CC1)N2CCC(CC2)C(=O)N. You can modify both the core and the substituents. Return only the SMILES as a Python list. Don’t put in line breaks. Don't put the prompt into the reply. However, when analyzing molecules created by general-purpose LLMs, I'm reminded of my undergraduate Chemistry days. My roommates, who majored in liberal arts, would often assemble random pieces from my mole...
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