Platform

Computer aided drug design, narrowed to one question

Most computer aided drug design software is general purpose: a target, a pocket, a docking score. Antibacterial is narrow on purpose. Everything here exists to answer one question well, which of these compounds is worth a plate against which strain.

See pricing

Capabilities

Six things, done properly

01

Strain panels

ESKAPE, Gram-negative and Gram-positive sets out of the box, custom panels from Discovery, and your own internal isolate library on Enterprise. The panel is the axis of everything else.

02

Predicted MIC bands

A doubling dilution band per strain in µg/mL, matching the resolution a broth microdilution plate actually has. Never a single decimal pretending to be a measurement.

03

Resistance risk flags

The mechanism the model expects, named: efflux and which pump, a carbapenemase family, porin loss, a target mutation, an acquired operon. A flag without a mechanism is not actionable.

04

Analog and literature lookup

The closest published analogs the model can genuinely name, with their reported activity, so you can see what the prediction is standing on.

05

Batch runs and ranking

Upload an SDF or CSV and get the series ranked by how much of the panel each compound holds, with the losses attributed to a mechanism.

06

Exports and API

CSV everywhere, SDF and a PDF report from Discovery, scheduled exports on Program. A REST API from Discovery so results land in your registry, not in a screenshot.

Strain panels

The axis of the whole product

A target based tool asks whether a compound binds. An antibacterial program has to ask whether it works on an organism that has an outer membrane, three efflux pumps and a plasmid. That is a different axis, and it is the one every read-out here is organised around.

Custom panels arrive on Discovery. If your collection has its own isolates with their own resistance profile, Enterprise loads them so the matrix matches your bench.

Why Gram-negatives are the hard case

ESKAPE

Six pathogens behind most resistant hospital infections.

S. aureus (MRSA)
E. coli
K. pneumoniae (CRE)
A. baumannii
P. aeruginosa
E. faecium (VRE)

Gram-negative

Permeability and efflux decide almost everything here.

E. coli
K. pneumoniae (CRE)
A. baumannii
P. aeruginosa
E. cloacae
S. Typhimurium

Gram-positive

Target modification and acquired operons dominate.

S. aureus (MRSA)
S. aureus (MSSA)
E. faecium (VRE)
E. faecalis
S. pneumoniae
S. epidermidis

Anatomy of a read-out

Five fields per strain, and what each one is for

Field	Example	What it is for
Predicted MIC band	0.5-2 µg/mL	The concentration range where growth is expected to stop, at plate resolution.
Call	I Intermediate	A susceptible, intermediate or resistant read at the usual clinical breakpoint for that species. ND when there is no basis.
Resistance risk	efflux, MexAB-OprM	The mechanism most likely to take the compound out on that organism. This is the line that kills or saves a scaffold.
Reason	One sentence	Why the call is what it is, in language you can paste into a program meeting.
Confidence	3 of 4	How much published support sits behind the row. Low confidence is shown, not hidden.

The whole read-out carries one standing label: computational prediction from published literature, research use only. See the method for how each field is derived.

Getting data in and out

Nothing you put in is locked in

Input: SMILES, InChI, compound name, SDF, CSV
Batch size: 50 on Lab, 1,000 on Discovery, 10,000 on Program
Export: CSV, SDF, PDF report, scheduled exports on Program
API: REST with an API key, from Discovery upwards
Rate: Plan level monthly screen allowance, no per call surcharge

Governance

The part your chemistry team will ask about

Training: Submitted structures are never used to train any model
Sharing: Never shared with other customers
Deletion: Deletable on request
SSO: SAML 2.0 and OIDC on Program and Enterprise
Audit: Full audit log on Program and Enterprise
Deployment: Private or VPC deployment on Enterprise

Full security page

Try it here

The product, not a picture of it

This is the same screen that runs on the homepage. Change the compound, change the panel, and read the matrix.

Run a screen

of 3 left

Compound, by name or SMILES

Strain panel

S. aureus (MRSA) E. coli K. pneumoniae (CRE) A. baumannii P. aeruginosa E. faecium (VRE)

Read-out

Include closest published analogs

No account needed. 3 screens per session.

What comes back

Rows: One per strain in the panel
Per row: MIC band, S I R call, mechanism
Support: Confidence 1 to 4 and analogs
Typical run: About ten seconds

Sample output Ciprofloxacin Fluoroquinolone

Worked example, replaced when you run

Strain	MIC (µg/mL)	Call	Resistance risk
S. aureus (MRSA)	0.5->32	R Resistant	target mutation, grlA and gyrA Fluoroquinolone resistance is widespread in methicillin resistant lineages.
E. coli	<=0.015-0.06	S Susceptible	efflux, AcrAB-TolC in resistant isolates Potent against wild type; qnr carriage and gyrA changes shift the band sharply.
K. pneumoniae (CRE)	0.5->64	R Resistant	gyrA mutation with plasmid qnr Carbapenemase producing isolates almost always carry quinolone resistance too.
A. baumannii	8->64	R Resistant	efflux, AdeABC Constitutive efflux plus target changes leave little room at achievable exposure.
P. aeruginosa	0.25-2	I Intermediate	efflux, MexAB-OprM Borderline: active on many isolates, lost quickly once efflux is derepressed.
E. faecium (VRE)	4->32	R Resistant	target mutation, parC Enterococci are intrinsically poor fluoroquinolone targets.

Why

Ciprofloxacin is a well characterised fluoroquinolone, so the Gram-negative bands are strongly supported by published activity data. The deciding factor across this panel is not target affinity but exposure: efflux in P. aeruginosa and A. baumannii, and acquired target mutation everywhere resistance is already common. Against a modern ESKAPE panel it reads as a Gram-negative agent with two reliable losses.

Closest published analogs

Levofloxacin Broadly similar Gram-negative bands, better Gram-positive coverage
Delafloxacin Retains activity against many ciprofloxacin resistant staphylococci

Strain	MIC (µg/mL)	Call	Resistance risk	Conf.

Why

Closest published analogs

Save this screen and run the rest of your series. screen left in this session. This session is used up.

Computational prediction from published literature. Research use only, not a lab measurement and not clinical guidance.

Related pages

Bring a series, not just a compound

Batch upload ranks the whole set and attributes each loss to a mechanism. Start with one screen on the homepage, then create an account when you want the rest.

Run a screen first

What you are agreeing to

Your compounds stay yours
Never used for model training
Deletable on request
No card required to run a screen