Full-Time

Cheminformatician

Drug Discovery Platform

Confirmed live in the last 24 hours

Atomwise

Atomwise

11-50 employees

AI-driven drug discovery platform

Compensation Overview

$160k - $230k/yr

Mid, Senior

San Francisco, CA, USA

Category
Computational Biology
Biology Lab & Research
Biology & Biotech
Required Skills
Python
Git
Machine Learning
Linux/Unix
Connection
Connection
Connection
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Requirements
  • M.S. or Ph.D. in Biochemistry, Computational Chemistry, Computer Science, Mathematics, Physics, Structural Biology, or a related field.
  • 3+ years of industry experience or post-graduate work.
  • Biomolecular modeling experience in one or more of the following: molecular docking, structure-based virtual screening, molecular dynamics simulations, homology modeling.
  • Hands-on experience working with protein structure data and common software in molecular modeling (e.g. RDKit, Biopython, Rosetta, molecular simulation packages, or similar).
  • Hands-on experience with common data science software stacks (e.g., Python, Jupyter notebooks, git, etc) and with using software development best practices.
  • Strong knowledge of cheminformatics principles and techniques such as molecular property prediction, similarity searching, molecular diversity, QSAR analysis, protein-ligand interaction analysis; experience with ADMET data is a plus.
  • Strong programming skills in Python and/or other scripting languages, and familiarity with the Linux command-line environment.
  • Experience working with scalable algorithms using large datasets is a plus.
  • Experience with cloud-based computing is a plus.
Responsibilities
  • Develop, maintain, and curate cheminformatics databases and tools, particularly for ultra-large chemical libraries.
  • Train and validate machine learning models on chemistry and biology datasets.
  • Design and refine benchmark datasets and analyses to track model performance on drug discovery problems such as potency and property prediction.
  • Design effective data visualizations to communicate data analysis and results to audiences with a wide range of scientific backgrounds.
  • Collaborate with machine learning researchers and software engineers to develop, release, and maintain new tools.
  • Contribute cheminformatics expertise to project teams, including data analysis, visualization, and interpretation.
Desired Qualifications
  • Experience working with scalable algorithms using large datasets is a plus.
  • Experience with cloud-based computing is a plus.

Atomwise uses artificial intelligence to enhance drug discovery in the pharmaceutical industry. Its main product is an AI engine that employs machine learning and convolutional neural networks to analyze extensive chemical libraries. This technology allows Atomwise to identify new small molecule medicines more quickly and accurately than traditional methods. The company primarily serves clients in healthcare and pharmaceuticals, focusing on developing and co-developing drug candidates that are funded by investors. Atomwise stands out from competitors by integrating AI and machine learning into the drug discovery process, which not only speeds up the development of new medicines but also aims to improve patient outcomes with more effective treatments. The goal of Atomwise is to transform the drug discovery landscape, making it more efficient and impactful for patients globally.

Company Size

11-50

Company Stage

N/A

Total Funding

$196.7M

Headquarters

San Francisco, California

Founded

2012

Simplify Jobs

Simplify's Take

What believers are saying

  • AtomNet's success rate surpasses traditional methods, attracting more pharmaceutical partnerships.
  • Experienced leaders like Steve Worland and Gavin Hirst drive strategic growth.
  • Atomwise's shift to proprietary drug development could increase its market presence.

What critics are saying

  • Competition from AI-driven companies like Insilico Medicine and Exscientia is increasing.
  • Scaling operations for proprietary drug development may pose challenges.
  • Regulatory hurdles for AI-generated drugs could delay approvals.

What makes Atomwise unique

  • Atomwise uses AI to discover small-molecule drugs, unlike traditional methods.
  • AtomNet technology unlocks more undruggable targets than other AI platforms.
  • Atomwise collaborates with over 250 partners globally, enhancing its drug discovery capabilities.

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Benefits

Medical, dental, & vision

FSA

401k

Parental leave

Commuter benefits

Professional development

Growth & Insights and Company News

Headcount

6 month growth

-5%

1 year growth

-11%

2 year growth

2%
Business Wire
Feb 18th, 2025
Atomwise Appoints Steve Worland, Ph.D. as Chief Executive Officer

SAN FRANCISCO-(BUSINESS WIRE)-Atomwise, a leader in leveraging machine learning and artificial intelligence (ML/AI) to advance small molecule drug discovery, today announced the appointment of Steve Worland, Ph.D. as Chief Executive Officer and member of the Board of Directors.

Business Wire
Apr 2nd, 2024
Atomwise Publishes Results From 318-Target Study Showcasing Atomnet Ai Platform’S Ability To Discover Structurally Novel Chemical Matter

SAN FRANCISCO--(BUSINESS WIRE)--Atomwise announced today results from the AIMS (Artificial Intelligence Molecular Screen) initiative that establish the AtomNet AI Platform as a viable alternative to high-throughput screening (HTS) and verify its ability to consistently discover structurally novel chemical matter. The landmark study applied AtomNet to 318 targets through collaborations with over 250 academic labs across 30 countries, representing the largest and most comprehensive virtual HTS campaign reported to date. In a paper published in Nature Scientific Reports, AtomNet successfully identified structurally novel hits for 235 of 318 targets evaluated, representing a significant improvement in hit success rate over traditional HTS. AtomNet demonstrated consistently high hit rates across all target classes, which covered a wide breadth of protein classes relevant across major therapeutic areas, confirming the broad applicability of the AI platform. AtomNet also showed a remarkable ability to discover novel chemical matter, averaging over seven structurally distinct bioactive compounds per target. Developed by Atomwise, AtomNet was the first deep neural network designed to predict the bioactivity of small molecules in structure-based drug discovery

Endpoints News
Oct 2nd, 2023
Ex­clu­sive: 'Faster is­n't bet­ter' - Atom­wise moves from part­ner­ships to pipeline with TYK2 drug

Atom­wise has hired Neely Mozaf­far­i­an as its chief med­ical of­fi­cer, CEO Abra­ham Heifets told End­points News in an ex­clu­sive in­ter­view on his com­pa­ny's fu­ture.

BioPharmaTrend
Mar 23rd, 2023
The State of A.I. Drug Discovery and Its Future: Small Molecules, Vaccines, and Antibodies

An active A.I. driven drug development company since the beginning, Atomwise recently introduced A.I. based 'AtomNet PoseRanker (ANPR)' with improved performance.

Business Wire
Sep 14th, 2022
Atomwise Appoints Gavin Hirst, Ph.D., As Chief Scientific Officer

SAN FRANCISCO--(BUSINESS WIRE)--Atomwise, a leader in using artificial intelligence (AI) for small molecule drug discovery, today announced the appointment of Gavin Hirst, Ph.D., as Chief Scientific Officer. Dr. Hirst brings more than 30 years of expertise in drug discovery across a range of therapeutic areas. He was instrumental in the discovery and development of lorpucitinib and mivatonib, and is co-inventor of vaborbactam, which was approved by the U.S. Food and Drug Administration in combination with meropenem. Most recently, Dr. Hirst served as Interim CSO of Turning Point Therapeutics, which was acquired in August by Bristol Myers Squibb for $4.1 billion. “At Atomwise, we continue to reimagine how drug discovery should be done, now that our AI-enabled engine allows us to interrogate novel chemical space and gives us the potential to drug challenging targets,” said Abraham Heifets, Ph.D., Co-Founder and CEO of Atomwise. “Gavin brings deep expertise in structure- and fragment-based drug discovery, and a fantastic track record in driving those initial discoveries to become new medicines to help patients. We are thrilled that he is joining our team.”