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Up Directory CCL 17.09.08 Structure- and ligand-based multi-target machine learning for virtual screening
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To: jobs at ccl.net
Date: Fri Sep 8 16:21:58 2017
Subject: 17.09.08 Structure- and ligand-based multi-target machine learning for virtual screening
Applications are invited for an industrial postdoctoral 
position under Dr. Valery Polyakov and Dr. Eric Martin at 
Novartis Institutes for Biomedical Research in Emeryville, 
California. This interdisciplinary postdoctoral opportunity in 
Computational Sciences will work at the interface of 
structure-based drug design and machine learning. 

Inexpensive computational virtual screens by docking have 
not had the accuracy needed to replace expensive and 
time-consuming experimental high-throughput screens. 
QSAR can be accurate for compounds similar to the training 
set, but not for interesting novel chemical matter. Our lab is 
using multi-target machine learning to overcome these 
limitations. AutoShim creates accurate, target customized 
scoring functions by adjusting the weights of 
pharmacophore shims in a protein binding site to reproduce 
several hundred training IC50s.[1] Kinase Ensemble Surrogate 
AutoShim pre-docks the screening collection into an 
ensemble of 8 diverse representative kinases, which can 
then be "shimmed" to predict the activities of the entire 
collection on all other kinases with training data, without 
requiring further docking or even protein structures.[2] Profile-
QSAR is a 2D multi-target ligand-based method that 
expands the domain of applicability of empirical models to 
the entire company archive by using predicted activity from 
thousands of conventional single-assay 2D QSAR models 
as compound descriptors for modeling new assays.[3,4]

This project will expand on these methodologies as well as 
developing new approaches.

The qualified candidate will be a highly motivated, creative 
and independent computational chemist with expertise in 
modern machine learning methods including multi-target 
deep neural networks. The candidate should be a 
competent programmer familiar both with procedural 
languages, ideally python, as well as machine learning 
packages including scikit learn or R and tensorflow. 
Familiarity with docking will be important, and 3D QSAR 
will be a plus.

1. Martin, E. J.; Sullivan, D. C., AutoShim: empirically 
corrected scoring functions for quantitative docking with a 
crystal structure and IC50 training data. J. Chem. Inf. 
Model. 2008, 48, 861-872.

2. Martin, E. J.; Sullivan, D. C., Surrogate AutoShim: 
Predocking into a Universal Ensemble Kinase Receptor for 
Three Dimensional Activity Prediction, Very Quickly, 
without a Crystal Structure. J. Chem. Inf. Model. 2008, 48, 
873-881.

3. Martin, E.; Mukherjee, P.; Sullivan, D.; Jansen, J., 
Profile-QSAR: a novel meta-QSAR method that combines 
activities across the kinase family to accurately predict 
affinity, selectivity, and cellular activity. J Chem Inf Model 
2011, 51, 1942-56.

4. Martin E.J.; Polyakov, V.R.; Tian, L.; Perez, R.C., 
Profile-QSAR 2.0: Kinase Virtual Screening Accuracy 
Comparable to Four-Concentration IC50s for Realistically 
Novel Compounds. J Chem Inf Model 2017, DOI: 
10.1021/acs.jcim.7b00166.
The position will start in January 2018. Please apply through 
http://postdoc.nibr.com/eric-martin.html
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