De Novo Small Molecule Design

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Overview

De novo design generates molecules that do not exist in any catalogue, shaped to a specific binding site. We use structure-conditioned generative models to propose chemistry that fits the pocket, then filter hard: synthetic accessibility, drug-likeness, predicted ADMET, novelty against published and patented matter, and re-docking of every survivor. Generation is the cheap part of this service; the value lies in the filtering and in a candidate list a chemist is willing to make.

Schematic: generation is the cheap step. The value is in the filtering — synthesisability, properties, novelty — and in confirming every delivered design.

Key Advantages

Pocket-Conditioned Generation

Molecules are generated in the context of the three-dimensional site rather than sampled from a chemical distribution and docked afterwards, which improves shape and interaction complementarity.

Synthesis Realism Enforced

Every proposal is scored for synthetic accessibility and checked by retrosynthetic analysis, with a route sketch for the final set so proposals are not merely elegant on screen.

Multi-Objective From The Start

Potency, physicochemical properties, ADMET flags and novelty are optimised jointly, avoiding the familiar outcome of a high-scoring binder that fails on solubility or metabolism.

Novelty Checked Explicitly

Generated scaffolds are compared against known actives and patent-adjacent chemotypes so you learn which proposals are genuinely new and which merely rediscover the literature.

Where This Service Is Used

Generating chemical starting points when virtual screening of existing libraries has failed

Designing around a competitor patent while keeping the interactions that matter

Growing, linking or merging fragment hits into lead-like molecules

Scaffold hopping away from a series with a fixed liability such as hERG or metabolic instability

Exploring a newly discovered allosteric or cryptic pocket with no known chemical matter

Methods and Techniques Applied

Binding site preparation and definition of pharmacophoric constraints from hot spots or known ligands

Structure-conditioned generative sampling, typically producing tens of thousands of raw candidates

Filtering for validity, drug-likeness, property ranges, PAINS, reactive groups and predicted ADMET liabilities

Synthetic accessibility scoring and retrosynthetic route assessment for the surviving set

Re-docking and, for the final tier, MM/GBSA rescoring and short MD stability checks

Scaffold clustering, novelty and freedom-to-operate style similarity analysis, and final ranked selection

What We Need From You

Target structure with the site to be designed against, ideally with a reference ligand pose

Known actives and the SAR behind them, which strongly improves the relevance of what is generated

Property and liability constraints: molecular weight and logP ranges, chemistry to avoid, required selectivity

Synthesis capability available to you, since it determines how ambitious the accepted routes can be

What You Receive

Ranked set of designed molecules with SMILES, docked poses and predicted property profiles

Retrosynthetic assessment with a proposed route for the recommended compounds

Novelty analysis against known actives, published series and patent-adjacent chemotypes

Interaction analysis explaining what each design is intended to achieve in the pocket

Generation and filtering report with model choices, thresholds and the attrition at each stage

What We Compute and Report

Generated structures with three-dimensional poses in the target pocket

Predicted property profile per design: lipophilicity, solubility, polar surface area, drug-likeness flags

Synthetic accessibility score with a retrosynthetic route assessment for recommended designs

Novelty and similarity distance to known actives and published series

Re-docking score and interaction analysis confirming every delivered design

Interpretation and Limitations

Computational predictions prioritise hypotheses; they do not independently establish binding, biological activity, therapeutic efficacy, safety or clinical validity. Performance depends on input quality, target class, the structural evidence available and the applicability domain of the selected methods. Scores used for ranking are comparative devices and should not be read as measured quantities. All deliverables are for research use only and are not intended for diagnostic or therapeutic use.

Specifications

Item	Specification	Notes
Molecules generated	10K–500K raw	Before filtering; the delivered set is far smaller
Final selection	20–100 designs	Sized to your synthesis capacity
Typical turnaround	4–8 weeks	Including filtering, rescoring and route assessment
Output	SDF, SMILES, report	With poses and property predictions per compound

Service Workflow

Step 01

Scientific Consultation

We discuss the objective, the decision it supports and the data you hold. Nothing is quoted before we understand what the result has to answer.

Step 02

Feasibility Review

We check that the data, target class and available structural evidence can support the proposed approach, and say plainly where they cannot.

Step 03

Technical Proposal

Scope, methods, deliverables, timeline and price are confirmed in writing, together with the limitations we expect to report.

Step 04

Computation and QC

The analysis runs on pinned tool versions in a containerised environment, with quality checkpoints and method comparison where that is informative.

Step 05

Report and Follow-Up

You receive the ranked results, the documented methods, the limitations and a proposed validation plan, plus support in interpreting them.

Is This the Right Service for You?

Choose this service when

Screening has not produced usable chemical matter for a well-characterised pocket

Fragment hits exist and need growing while preserving a crystallographic binding mode

A series must escape a patent position without losing the interactions that drive potency

Consider another service when

Purchasable compounds would answer the question faster

High-Throughput Virtual Screening

A scaffold is fixed and the task is multi-property optimisation

Reinforcement Learning for Molecule Optimization

What Goes Wrong, and How We Handle It

The failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.

Accessibility scoring plus retrosynthetic assessment is applied before delivery, and the recommended tier ships with a route sketch.

Case Studies

The situation

Crystallography had placed two weak fragments in adjacent sub-pockets, separated by a narrow channel.

What the work involved

Both crystallographic poses were held fixed while linker chemistry was generated into the channel, then filtered for accessibility, strain and properties, and every survivor was re-docked and simulated briefly.

What came back

Forty designs ranked by predicted fit and makeability, with the six shortest synthetic routes highlighted and the vectors left open for later optimisation identified.

Illustrative of the shape this work takes. Details are generalised, no client or target is identified, and scope and outcome vary with the system in question.

Questions We Are Asked Before Scoping

Note: ICDMO delivers computational (in silico) services. Results prioritise hypotheses for testing; they do not by themselves establish binding, biological activity, efficacy or safety. All deliverables are for research use only and are not intended for diagnostic or therapeutic use. Downstream experimental work is scoped separately and executed through qualified partners.

Get in Touch
about De Novo Small Molecule Design

Tell us about the target, the data you hold and the decision the result needs to support. A scientist reviews every enquiry and replies within one business day.

Feasibility reviewed before any scope or price is fixed

Methods, parameters, confidence and limitations in every report

Confidentiality agreement available before data is shared