Overview
News
Technologies
Salaries
Products
People
Growth
Offices
Financials
Overview
Superluminal Medicines is a software led drug discovery company focused on helping researchers find and develop new medicines more efficiently. From its positioning and messaging, the core problem it tackles is the time and cost involved in identifying promising drug candidates and understanding how they might behave, a process that is often slowed down by fragmented data, complex biology, and long experimental cycles. Superluminal appears to address this by building computational tools that support decision making earlier in the discovery pipeline, aiming to help teams prioritise the most viable options before committing heavily to lab work.
News
"Biopharma returns to MC4R for drugs targeting genetic obesity as well as appetite loss." Featured Story on Endpoints News by Kyle LaHucik
The post "Biopharma returns to MC4R for drugs targeting genetic obesity as well as appetite loss." Featured Story on Endpoints News by Kyle LaHucik appeared first on Superluminal.
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Report
Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks: Published in Communications Chemistry
The post Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks: Published in Communications Chemistry appeared first on Superluminal.
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Report
Optimizing drug design by merging generative AI with a physics-based active learning framework
Machine learning is transforming drug discovery, with generative models (GMs) gaining attention for their ability to design molecules with specific properties. However, GMs often struggle with target engagement, synthetic accessibility, or generalization.
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Report
Technologies
Tech DNA
Python
PyTorch
Ray
AWS
AI/ML
Python
AWS
Mixture of Experts
NumPy
PyTorch
Ray
Scikit-learn
Other
Microsoft Excel
Stack modernity
72/100
How modern this stack is, based on technology relevance, AI adoption and the share of legacy tools.
In-demand technologies
Microsoft Excel
1 job
Required
Python
1 job
Optional
Scikit-learn
1 job
Optional
NumPy
1 job
Optional
PyTorch
1 job
Optional
AWS
1 job
Optional
Ray
1 job
Optional
Mixture of Experts
1 job
Optional
Salary medians are calculated from this company's open jobs and compared with the market.
Industry adoption
Python
28%
AWS
21%
PyTorch
18%
NumPy
9%
Scikit-learn
8%
Ray
6%
Share of companies in the same industry that use each technology.
Stack changes
Microsoft Excel
Sep 2026
Scikit-learn
Sep 2026
Ray
Sep 2026
PyTorch
Sep 2026
Python
Sep 2026
NumPy
Sep 2026
Mixture of Experts
Sep 2026
AWS
Sep 2026
Technologies recently added to or removed from this company's stack - a signal of tech migrations and new initiatives.
Growth
Hiring Momentum
58/100
Growing
Open positions
6
0 opened / 0 closed in 30 days
ATS activity
Every ~2 days
09/08/2026
Hiring Dynamics
+100%
Hiring Focus
The percentage next to each role is its share of the company's job openings over the last 90 days; the arrow shows the shift versus the previous period.
Finance
50% ▼
Operations
25% ▲
Science & Research
25% ▼
Activity Timeline
Added Scikit-learn to stack
Sep 2026
Added Ray to stack
Sep 2026
Added PyTorch to stack
Sep 2026
Added Python to stack
Sep 2026
Added NumPy to stack
Sep 2026
Added Mixture of Experts to stack
Sep 2026
Added AWS to stack
Sep 2026
Offices
Where the company hires and what each office is for
Cities
1
Hiring now
1
Countries
1
Busiest office
Boston
Boston
United States
Regional office
Boston, MA
6 open roles 4 posted in 90 days 50% engineering
hires for Science & Research, Finance, Operations

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