02. AI-driven Drug Design (AIDD) enabled drug discovery and optimization

AI-driven Drug Design (AIDD) enabled drug discovery and optimization

Utilizing our state-of-the-art AI-driven Drug Design (AIDD) platform, our experts will work with you to customize your project and deliver molecules with the combination of properties you require. Our AIDD platform integrates our advanced generative chemistry algorithms with our best-in-class high-throughput PBPK (powered by GastroPlus®) and ADMET predictions. AIDD performs iterative generation cycles coupled with Pareto front multi-objective compound optimization to deliver novel molecules that are active and lead-like.

Aren’t 100% sure what that all means? Don’t worry, our experts are here to help! We can assist with any stage of the process, from designing the very first molecules you will test, to performing the final optimization steps of a well-developed lead series to select a clinical candidate.

We start by meeting to review your project and what data you have already generated. We will determine what stage you’re at and what additional steps are necessary to get you where you want to go.

We will perform a careful review of the academic and patent literature around your target, and curate and add relevant data from the literature to increase the size of our dataset. This can improve the predictive ability of models we build. Any data we discover will be discussed with you to ensure its validity and appropriateness for incorporation into models. We will carefully standardize all data by neutralizing compounds, finding preferred tautomers, removing metal ions and salts, and removing duplicates.

We will then use our AI algorithms to construct QSAR models based on your data, that which we find from literature, and our novel molecular/atomic descriptors. Conversely, we can incorporate any QSAR/QSPR models that you may have generated internally.

Prior to performing the first AIDD run, we’ll put on our medicinal chemist hats and closely inspect the data to better guide the AIDD process. This includes utilizing best-in-class cheminformatic toolkits, identifying key activity cliffs via matched molecular pair analysis (MMPA), generating classes and scaffolds for each class, then generating and analyzing R-tables to provide clues for which parts of the molecule may be the best to evolve.

We will then perform our first AIDD run with parameters for optimization decided upon collaboratively.

We typically optimize 4-5 parameters simultaneously, the most common parameters being target activity, ADMET Risk™, preclinical/clinical PK endpoints (e.g., bioavailability) and synthetic difficulty. However, there are over 100 parameters for which your molecules can be optimized.

Our experts will then filter results for novelty (by similarity searches against patent databases) and commercial availability (by searching chemical manufacturer databases such as Enamine REAL). If these searches limit the potential number of molecules, a second round of AIDD will be performed.

Following our extensive AIDD enabled design and optimization, we present you with a ranked list of potential molecules (based on agreed-to parameters) to be purchased and tested. If you want, we can facilitate the purchase of the compounds for you. Furthermore, if you want to experimentally determine any other properties of the compounds (e.g. hERG activity, microsomal clearance rate), we can facilitate such experiments via our partner CROs.

After you have tested the molecules, we meet to discuss the results and next steps, whether that be further AIDD optimization or, if you have your optimized lead and are ready for IND-enabling studies, an introduction to our experts who can help with all the next stages of your drug development journey, from PBBM / PBPK modeling & simulation to post-approval bioequivalence studies.

03. HTS library design and HTS hit visualization/analysis

HTS library design and HTS hit visualization/analysis

Often drug discovery begins with a high-throughput screen. Our experts can work with your screening and chemistry teams to help build the best libraries for your target of interest, and analyze the results from the screen to select the most promising leads for further optimization.

Our experts will begin the collaborative research project by meeting with your team to discuss the overall goals of the project, the HTS assay design, makeup of the initial screening library, and any initial screening results should you already have them. We can then help you design an initial screening library or follow-on/focused libraries and/or analyze the hits you have to select the most promising leads for further development.

We understand the quality of your screening library significantly impacts your results. It is important to consider not only the chemical space appropriate for your target, but also the physiochemical properties of the compounds themselves. Successful translation from hit to lead requires that the compound have appropriate ADMET and PK properties. We can use our industry-leading ADMET Predictor platform to predict important ADMET and PK properties and limit library constituents to only those meeting defined thresholds, thus increasing the likelihood of hits that are lead-like/drug-like. We can also filter results by commercial availability, whether it be fully enumerated compounds or building blocks for combinatorial chemistry approaches.

We can also help design follow-on or focused library builds after a diverse compound library HTS. Using data derived from the initial hit population, we can apply our combinatorial chemistry library enumeration tool in the MedChem Studio™ Module to create virtual libraries and work with you to facilitate purchase of compounds to build that library.

Analyzing the results of a HTS campaign is also a crucial step in the lead discovery process. Our team has tools to streamline HTS hit analysis and maximize the chance of downstream success. As a first step, we can quickly and accurately predict more than 100 important PK and ADMET properties for every hit, including our unrivalled pKa prediction, bioavailability, as well as synthetic difficulty. We also provide easy-to-comprehend risk plots for ADMET Risk™, toxicity risk, CYP inhibition risk, and more. We can generate classes containing the same scaffold, predict those same classes, and compare relative activity and risk among the classes. This can help with the selection of a lead scaffold or scaffolds for further optimization. Furthermore, MMPA can be performed to identify important activity cliffs to guide future evolution of the lead molecule(s).

Let us help your team decide the best candidates for further testing and optimization. Data from HTS campaigns can also be used to build QSAR models using ADMET Modeler™, which can help inform additional library builds and medicinal chemistry optimization efforts.

After discussing your team’s needs and goals, we will get to work designing libraries and/or analyzing your hits. For library design projects, we will provide a database file with all necessary information about the compounds, including unique identifiers (we can work with you and your indexing system), structural information, and all our ADMET and PK predictions, along with a summary of the design process. We can also include links to commercial vendors, if requested.

For HTS analysis projects, we will provide you with a ranked list of compounds and/or scaffolds best suited for further development, based on agreed-to parameters. This list will include all our ADMET and PK predictions, along with rationale for selection.

For either service, we will meet with your team to discuss the results of our work, answer any questions you have about our reports, and revisit the project if necessary. Following acquisition and rescreening or testing of library or hit compounds, we will meet again with your team to discuss results and next steps, whether that be additional rounds of screening, further hit optimization, or advancing a clinical candidate to pre-clinical testing.

04. QSAR/QSPR modeling and simulation

QSAR/QSPR modeling and simulation

Our consulting experts have decades of experience using our AI/ML algorithms to generate accurate and robust QSAR/QSPR models for clients working on biopharmaceutical products to cosmetics and everything in between. From models of on-target activity and selectivity to in vivo clearance and other PK properties, our team has the experience to deliver the predictive models you need for success.

A typical project begins by meeting with your team to discuss the overall goals of the project and data you have generated that could be used for model building. We can also perform a careful review of the academic and patent literature around your target, and curate and add relevant data from the literature to increase the size of our dataset and improve the predictive ability of models we build. Any data we discover will be discussed with you to ensure its validity and appropriateness for incorporation into models. We will also work with you to decide how the data should be divided for training and testing, and what method(s) are most appropriate for parsing of the data.

After collaboratively curating the dataset, we will carefully standardize all data by neutralizing compounds, finding preferred tautomers, removing metal ions and salts, and removing duplicates. Next, we will use our custom AI/ML algorithms to serially develop predictive models, using results from one round of builds to inform subsequent builds. We have a variety of techniques to generate both classification and regression models to ensure the best possible models are created.

We can also predict a spectrum of ADMET and PK properties with ADMET Predictor®, including pKa(s), solubility vs. pH, logD vs. pH, CYP & UGT metabolism/inhibition, Ames mutagenicity, skin and respiratory sensitivity, rat and mouse TD50, and much more. Descriptor and structure sensitivity analysis (DSA and SSA) tools can then be used to determine what atomic and molecular parameters contribute most to the QSAR/QSPR models and which atoms in individual structures contribute most to the model score, which can be used to guide future lead optimization efforts.

After generating QSAR/QSPR models, we will provide you with a model or models that meet agreed-upon criteria, including cutoffs for specificity and sensitivity, and other important statistical measures. We will provide models in a format that is compatible with your in-house systems, along with all relevant documentation describing how the model was built, the descriptors used to build the models, performance characteristics, training/test sets, etc. We will meet with you to discuss and help you select the best models for your continuing studies. We can provide access to DSA and SSA tools and offer advice on how these analyses can inform future lead optimization efforts.

When you have settled on a lead compound (and several backups), you will likely begin to measure some, but not all, ADMET and PK endpoints. We can help you fill in the gaps with in silico predictions and use this information to decide which experiments to run next.

For those of you in the industrial chemicals, agrochemicals, and cosmetic markets, as your project advances and you prepare to file your chemical registration dossier, the burden falls on you to assess the potential hazards and safety risks associated with your compound (e.g., the REACH European regulation). Fortunately, there are opportunities to use our industry-leading toxicity predictions in ADMET Predictor to waive some of the tests required by health authorities. Also, we can incorporate next generation risk assessment (NGRA) by applying our HTPK Simulation technology, which combines the QSAR models with the GastroPlus® PBPK modeling platform, earlier in your project to predict in vivo exposure in animals or humans to calculate the Margin of Safety (MoS).

05. Experts

Meet the Experts