https://github.com/merck/compoundcomplexity

This is an implementation of Compound Complexity for use in the SMART-PMI as described by Sherer et al. It contains derived training data as required by the described Random Forest Model in order to replicate data presented in paper as well as applying to novel data.
https://github.com/merck/compoundcomplexity

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This is an implementation of Compound Complexity for use in the SMART-PMI as described by Sherer et al. It contains derived training data as required by the described Random Forest Model in order to replicate data presented in paper as well as applying to novel data.

• Host: GitHub
• URL: https://github.com/merck/compoundcomplexity
• Owner: Merck
• License: other
• Created: 2021-09-08T19:28:00.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2021-12-22T21:42:46.000Z (over 4 years ago)
• Last Synced: 2023-06-12T11:00:38.781Z (almost 3 years ago)
• Language: Perl
• Size: 3.1 MB
• Stars: 4
• Watchers: 5
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README
  • License: LICENSE

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README

          
Compound Complexity Calculator

USAGE: bin/compoundcomplexity.sh [ -sdf  | -smi  ]

NOTES: This is an implementation of Compound Complexity for use in the 

  SMART-PMI as described by Sherer et al. 

  It contains derived training data as required by the described Random Forest

  Model in order to replicate data presented in paper as well as applying to 

  novel data.

  Use of SDF input is strongly recommended.

  Eq 1.

          SMART-PMI = (0.13 × MW) + (177 × Complexity) - 252

ABSTRACT: An important metric for gauging the impact a synthetic route has on

  chemical resources, cost, and sustainability is process mass intensity

  (PMI). Calculating the overall PMI or step PMI for a given synthesis from a

  process description is more and more common across the industry. Our company

  has established a strong track record of delivering on our Corporate

  Sustainability goals, being recognized with seven EPA Green Chemistry

  Challenge Awards in the last 15 years. While green chemistry principles help

  in optimizing PMI and developing more sustainable processes, a key challenge

  for the field is defining what ‘good’ looks like for any given molecule.

  Predicting aspirational PMI for a synthetic target is not yet possible from

  chemical structure alone. The only tool chemists have at their disposal to

  predict PMI requires the synthetic route to be available, which is inherently

  retrospective. We have developed SMART-PMI (in-Silico MSD Aspirational

  Research Tool) to fill this glaring gap. Using only a 2D chemical structure,

  which enables a measure of molecular complexity, we can generate a predicted

  SMART-PMI using historical PMI data from our company’s clinical and

  commercial portfolio of processes. From this SMART-PMI prediction, we have

  established target ranges for Successful, World Class, and Aspirational PMI.

  Using this model, chemists can develop powerful synthetic strategies that

  make the biggest impact on PMI and, in turn, drive improvements to the model.

  The potential of SMART-PMI to set industry-wide aspirational PMI targets is

  discussed

Requirements

- MOE (http://www.chemcomp.com)

- Perl 5.22.1+