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CRITICAL ASSESSMENT OF COMPUTATIONAL HIT-FINDING EXPERIMENTS

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Challenge #3

Hit Identification
Method type (check all that applies)
De novo design
Deep learning
Description of your approach (min 200 and max 800 words)

We  will  use  our  expertise  in  AI/ML, cheminformatics, structure-based  drug  design  (SBDD),  medicinal chemistry  to  generate  hits  for  NSP3 Macrodomain (Mac1).  Using  our  in-house  drug discovery  &  cheminformatics  platform  (published  in  scientific  literature,  proprietary  code),  we will identify a suitable subset of compounds from the Enamine Real Database using various filters which  follow medicinal  chemistry  standards  &  CACHE  white  paper  guidelines. We will implement two deep learning guided approaches to generate hit ideations, generated hits will be considered as template for sub-structure/similarity search to generate hit list.

Strategy 1 (AI-guided scaffold hopping): 

Earlier research efforts (1-4) led to the identification of several sub-micromolar inhibitors which can be potential starting points for scaffold-hopping, to identify novel IP space. We will implement a reinforcement learning approach (5-6) to generate scaffold ideas to be considered as templates for substructure search against the Enamine database. We will generate a potential hit list (bearing the scaffold moieties of our choice) from the substructure search results and those hits will be further filtered for fit using docking studies with Fitted (7-8).

Strategy 2 (Fragment merging/linking):

Prof. Fraser and his team’s efforts led to identification of 160 fragments bearing around 120 diverse scaffolds (2) and they reported 152 Mac1-ligand complex crystal structures from this study.  We will encode interaction patterns, binding modes, and scope for fragment-based expansion using these crystal structures and their complexed fragments. We will implement recurrent neural network (RNN)-based generative model (9-10) which is trained to propose plausible linker ideas for a given two input fragments. A major advantage of this generative model is how it can propose linker ideas that satisfy an interaction constraint with additional defined parameters such as. ring size, linker size, and property ranges (chemical space). Generated virtual hits will act as templates for structure search (substructure and similar) against the Enamine databases. Extracted compounds will be further filtered for fit using rigid docking studies with Fitted (7-8). If the generated hits are too dissimilar from the Enamine library, we will consider proposing custom synthesis of these compounds.

Hit selection criteria:

1.) We will carry out the rigid docking studies for both scaffold hopping and fragment-based strategies

2.) We will pick the top-scoring hits ranked based on our docking scoring function.

3.) We will employ a Quantum Mechanics-Based Scoring Function (QMSF) and re-rank the hits based on the calculated relative free energy of binding using a more accurate scoring function than a standard docking function. 

4.) We will use the retrospective data (known actives/fragments) and its interaction profile knowledge to shortlist and then re-rank the hits.

5.) Finally, our team will participate in a “hit-picking party”, wherein we will visualize the predicted poses and make a human-based selection following discussion and critique. 

6.) We plan to shortlist a total of 100 hits from both strategies (Scaffold Hopping and Fragment merging).

For the hit optimization stage, this workflow will change as follows: we will design the focused library around the potential hit compounds and filter the hits based on the property and structural filters. We will then undertake similar steps outlined above with the new, focused library.

Ultimately, we would like to establish multiple deep learning strategies (scaffold hopping approach and fragment merging strategy) to design potential inhibitors towards Mac1 domain. From each approach, each hit molecule is carefully assessed for its binding mode, key interactions with NSP3 Mac1 domain, and overall fit value. Total of 100 top ranking hits shortlisted from both approaches will be selected for procurement, and testing. "Computational negative controls" may also be selected to support our hypotheses. In line with the SGC/CACHE principles, we will document our research strategy, progress and publicize it for all to follow and reference; we are taking a research-centered focus with this opportunity. We hope the sharing of our findings will help guide future efforts in deep learning guided approaches and in SBDD. 

What makes your approach stand out from the community? (<100 words)

We will implement two different approaches to generate the hits as NSP3 Macrodomain (Mac1) inhibitors using deep learning (DL) guided approaches. DL-guided scaffold hopping approach to generate diverse scaffold ideas. In the second strategy, we will generate hit ideas using DL-guided fragment merging approach. Hit ideas generated from both strategies were considered as template for sub-structure/similarity search to generate hit list. Generated hit list subjected for docking studies and best 100 hits will be shortlisted based on the binding pose review, interaction patterns, and medicinal chemistry expertise. These strategies will enable us to generate potential hits against NSP3 Macrodomain (Mac1)

Method Name
Deep Learning Approach
Commercial software packages used

in-house

Free software packages used

Gromacs : If MD needed

Python and Deep Learning packages: Tensorflow, Scikit, Pandas, and Numpy

Relevant publications of previous uses by your group of this software/method

1. Design,  synthesis  and  in  vitro  evaluation  of  novel  SARS-CoV-2  3CLpro  covalent  inhibitors(2022): https://doi.org/10.1016/j.ejmech.2021.1140462. 

2. Discovery     of     covalent     prolyl     oligopeptidase     boronic     ester     inhibitors(2020): https://doi.org/10.1016/j.ejmech.2019.111783

3. Integrated Synthetic, Biophysical, and Computational Investigations of Covalent Inhibitors of Prolyl   Oligopeptidase   and   Fibroblast   Activation   Protein   α(2019): https://doi.org/10.1021/acs.jmedchem.9b006424. 

4. Docking  Ligands  into  Flexible  and  Solvated  Macromolecules. 8.  Forming  New  Bonds –Challenges and Opportunities(2022): accepted, J. Chem. Inf.Model

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