Fig2 WhatWorksWhatDoesnt

In this report from 2006, Martin assesses how computational chemists can positively impact drug hunting projects by forming close collaborations with medicinal chemists. The report starts by summarising the computational calculations that were the most popular with Abbott medicinal chemists at the time and showed data that indicated how computationally cataloguing structural alerts as SMARTS reduced the number of flagged compounds within Abbott’s compound library over time. Furthermore, several examples were identified where models with low predictability were still deemed useful. For example, log P predictors were shown to have low accuracy but considered to be “good enough” by medicinal chemists to predict relative log P values within a series. The report also highlights examples where computational chemists offered useful insight that aided decision-making, even when the data or models were inaccurate. An example of this was the observation by a computational chemist that the available data for modelling a compound series had a narrow log P range, which led to the prioritisation of more polar compounds that were subsequently optimised into a lead.

In summary, this report is still an important read for computational and medicinal chemists who work together today, serving as a reminder that drug discovery projects benefit when knowledge and insight is shared between the two fields.

What Works and What Does Not: Lessons From Experience in a Pharmaceutical Company

QSAR & Combinatorial Science, 2006, 25, 1192-1200.

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