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Abstract
This paper presents a simplified model of iterative compound optimization in drug/agrochemical discovery. Compounds are represented as binary strings, with project evolution simulated through random bit changes. The model reproduces key statistical features of real projects, including activity distributions and time-series characteristics. This framework enables statistical simulation of compound optimization, potentially aiding project planning and resource estimation.
Supplementary materials
Title
Iterated Project Evolution Code
Description
A rudimentary Python (requires Python 3.6 or higher) program that produces a stream of binary strings that mimic the properties of the time ordered compounds produced during a drug/agrochemical optimisation project
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