A drug discovery project has various twist and turns in its iterative design and optimisation process that can cover various areas of chemical space. Understanding how these projects evolve overtime could be crucial in the development of new projects. It is also important to understand why projects fail and the parameters set that could effect the drug discovery projects and understanding the end-goal of the project. #BucketListPapers
Delaney created a model that attempts to replicate the process through a self-avoiding walk (SAW). As overall researchers are a lot better at documenting successes rather than failures it makes it difficult to understand the difference in projects that work and don’t. The parameters of the modelled project can be adapted and explored. The model trajectories can be modelled on Sammon plots and these are similar to real life projects. The figure below is an example of a random walk generated by Delaney. The green circle is the starting point and the red circle is the end point, in this instance after 1000 steps. The start of the sequence is green, the mid section is blue and the end steps of the sequence is in red. Delaney analysed the number of successes and failures of each SAW project, where a success is a project that reaches it’s specific target and a failure is one where the SAW terminates before reaching the projects target. This analysis showed that allowing projects to run for longer increases the number of successes per project. Additionally, when altering the departmental parameters they have little effect on the number of success per project but did have an impact on the number of projects per step. Self-avoiding walks can, therefore, be used to predict how a drug-discovery project would react with certain constraints to understand how potentially success or not it maybe.
Delaney: Modelling Iterative Compound Optimisation using a Self-Avoiding Walk
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