AI and chemical simulation-based
drug discovery support service

OVERVIEW
Drug2drugs is an AI and chemical simulation-based drug discovery support service which generates many
candidate compounds with different scaffolds from the structural information of one active compound.
Drug2drugs applies our proprietary AI-AAM (amino acid mapping) method, which is an innovative in silico methodthat combines amino acid interaction mapping with scaffold-hopping to identify structurally diverse compoundswith improved drug properties and pharmacological activity. There is the potential for improved bioactivity,efficacy, thermal stability, synthesizability, and so on, which could even unlock the possibilities for avoiding drug
toxicity and overhauling the drug delivery characteristics of the drug (i.e., potentially, a drug that must be injected
intravenously can now be ingested orally after lead optimization through drug2drugs, with similar or improved
pharmacological effects).

KEY FEATURESVALUE DRIVERS

Streamlined Lead Optimization

Our innovative AI-AAM technology simplifies the leadoptimization process, requiring only the structural
information of the bioactive region of the compound.

Efficient Drug Discovery &Development

Accelerate your drug development pipeline with ourability to quickly generate backup compounds and refine

Confidential and Secure

We require little to no experimental data from the clientto acquire results, and we prioritize the confidentiality
and security of your data throughout the process.

Significant time & cost savings in R&D

Increased chances of discoveringa successful drug candidate or
improving aspects of a drug

Does not require target protein
information, reducing the need toshare experimental data1

How does the AI-AAM method work?
The AI-AAM method consists of the computation of AAM descriptors and our de novo design AI structure generator.
1. AAM (Amino-Acid Mapping) Descriptors[1]AAM descriptors are the set of the 3D
existence probability map of 20 aminoacid residues around each compoundcomputed by chemical simulations.
Two compounds with the same AAM
descriptors but different scaffolds
interact with the same amino acidresidues and bind to proteins with thesame binding pattern.

Performing chemical simulations with AAM descriptors allows us to quickly and efficiently predict bioactivity without
calculating the interaction with the target protein. Furthermore, the three-dimensional structures of protein–ligand
complexes is not required, and the calculation time is at least 10,000 times faster than QM/MM and takes only afew seconds per compound.
2. De Novo Design AI[2]Our structure design AI enables thegeneration of a wide variety of structures,
starting from nothing and gradually
growing compounds with similar AAM
descriptors to the AAM descriptor of thetargeted bioactive compound, atom byatom. With this atom-based design, thenumber of chemical spaces that can betargeted is 10^60, which is the theoreticallimit.
While this method of design often produces compounds that do not meet thermal stability requirements, we haveincorporated innovations to select for thermal stability for practical use.

Experimental Validation of the AI-AAM Method
1. Scaffold Hopping (Kinase: Syk[6])This first example shows a major
conversion of the core structure of alead compound while still maintaining
its
biologicalactivity.
Usingthekinase Syk inhibitor BIIB-057 as thesource compound, we searched fromapproximately 12 million compounds inthe commercial compound library andextracted XC608 as the compound withthe highest AAM similarity to BIIB-057.

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In addition, Compound A was also discovered by conducting a re-search using our in-house compound library.
The enzyme inhibitory activity of XC608 and Compound A was equivalent to that of the original compound, withlower IC50 values. These computations were completed in just one month.
2. Partial Modification (Kinase: Syk[3,9])150 compounds with greater than 80%
AAM similarity to the original bioactive
ligand were designed with strategic
modifications. Out of these, 12 compounds
were chosen and synthesized, withenzyme activity measurements ranging
from 0.73 to 60 nM. Based on theseresults, it is possible to efficiently improve
bioactivity through the AI-AAM methodby repeating new design, synthesis andevaluation, or binding energy prediction
using compounds with stronger binding
affinity than Compound A as a newstarting point.
3. Partial Modification (Bacterial Membrane Protein[4,9])
In this example, 332 compounds withmore than 80% AAM similarity to theinitial bioactive ligand were designed withpartial modification of ligand. Out of thisselection, 5 compounds were synthesized
successfully, with the cell bioactivity
measured between 6.25 to 12.5 μM,
showcasing comparable or slightly
reduced bioactivity relative to the originalcompound.
4. Peptide to Small Molecule (Protein-Protein Interaction: PD-L1[10])The small molecule, FF-ATC-001, wasdesigned using our AI-AAM method fromthe partial AAM of the binding parts ofPeptide-71. The pKi per molecular weightof FF-ATC-001 was 9.2, compared to 4.7
for Peptide-71, a 2-fold increase, resulting
in a 2-fold increase in bioactivity efficiency
per molecular weight. We believe that thisis a very positive result for the strategy ofextracting and converting the importantparts of the interaction.
The effect on PD-L1 is limited because it is an extracellular target, but cyclic peptides generally have low
membrane permeability and cannot target intracellular proteins, making them difficult to make into oral drugs.
Here we use MDCK2 cells to show how their membrane permeability has changed: PD-L1 hardly permeatedthe membrane, whereas FF-ATC-001 showed favorable membrane permeability, demonstrating the potential forconversion from injection to oral drug delivery.

3Comparison with Other Drug Discovery Methods
One of the unique points about our service
is that the AI-AAM method does not require protein structures or primary sequenceof the target protein, nor does it requirelarge amounts of experimental data.
It only requires the structural informationof the bioactive compound to produce
results, accelerating your efforts in drug
discovery in a secure, yet efficient manner.

Service Flow and Deliverables
The customer will need to provide the structural information of the bioactive compound. Based on this information,
we will provide the following deliverables:
1. List of candidate compounds, including the chemical structure and AAM similarity
2. 3D images of the relationship between chemical group and AAM similarity.

The computation time takes 1 month, and upon completion, we provide the final report and other details in aconfidential manner. We will provide the customer with further support to understand the report. Furthermore, as
per a standard contract agreement, data will be used to train the AI model in a secure manner.

4Example Use Cases

CITATIONS[1] JP6826672B2. [2] JP7191969B2, JP7190498B2, and JP7116186B2. [3] Enzyme activity was measured by
Syk kinase enzyme system and ADP-Glo™ Kinase Assay (Promega, USA). [4] Cell bioactivity was measured
by bacterial growth assay. [5] P. Etrl and A. Schuffenhauer, J. Cheminfo. 2009, 1:8. [6]CBI Annual Meeting
2022, P08-14; bioRxiv (2023), doi:https://doi.org/10.1101/2023.07.03.547598. [7] WO 2009/136995. [8] WO
2011/035077. [9] CBI Annual Meeting 2023, P03-07. [10] CBI Annual Meeting 2022, P08-07, Excellent PosterAwards. [11] US20140294898 A1.

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