AI-Driven Pharma R&D

AI-Driven Pharma R&D Creating an AI-Ready Data Foundation with Dotmatics

Table of Contenst

Introduction

5

Pharma Embraces Artificial Intelligence

5

Overarching Goals for AI in Pharma R&D

5

Evidence of AI’s Growing Impact on Pharma

5

Evolution of AI

5

Key Opportunities for AI in Pharma R&D

5

Key Challenges to Applying AI in Pharma R&D

5

Strategy, Technology, and Expertise

5

Data Challenges

5

Model Concerns

5

Getting AI-Ready with Dotmatics

5

Dotmatics Luma—The Foundation for AI Readiness

5

Preparing Data for AI

5

Phased AI Adoption with Dotmatics

5

AI-Enabled Solution Spotlights

5

5

References

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Introduction Researchers can collect and process data at an unprecedented scale thanks to advances in technology and laboratory instrumentation.

For example, in drug discovery development and testing each new candidate creates terabytes of data at every stage.1 These massive datasets are typically collected with multiple technologies that each require their own sophisticated analysis techniques and computational tools to extract meaningful insights.

While this “big data” holds big potential, it also presents a myriad of challenges. Researchers must be able to easily collect their data and trust its quality; they must have secure ways to store and share data, and of course, they ultimately need to use that data to garner insights and drive innovation.

In pharma R&D, researchers are increasingly turning to artificial intelligence (AI) and machine learning (ML) to make sense of their massive datasets. In fact, more than one-third of large-pharma executives report using AI technologies.2 AI/ML technology can quickly process billions of data points, acting like a powerful flashlight that illuminates hidden patterns and insights that exist in vast amounts of data—allowing researchers to uncover and understand things that were previously too dark to see.

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The caveat with predictive and prescriptive AI technologies is that they rely on training data. That data must not only be plentiful, but it must also be trustworthy and machine-ready. For many companies, data struggles stand in their way of successful AI adoption, and therefore, the first step in their journey toward AI-driven R&D is adopting an infrastructure that can support large-scale data collection and model refinement. This typically means that companies must manage their data differently than they have in the past, which is often a huge hurdle.

These companies need a scalable, flexible, open R&D data platform that empowers them to collect and manage high-quality, well-labeled, AI-ready data across diverse teams—even those who are working with high volumes of disparate data and differing specialty technologies.

In this eBook, we review recent trends seen with AI in the pharmaceutical industry and explore how companies can best position themselves to ensure their R&D data are AI-ready.

Pharma Embraces Artificial Intelligence The development path for drugs in the traditional paradigm is slow, costly, and riddled with failures.

Typically, it takes more than 10 years and somewhere between $2.5-6.7 billion dollars to bring a drug to market. Only around 10% of compounds from early discovery make it to launch.3-5 What if we could take all the lessons learned from our past failures and successes to inform future drug design? What if AI could be used to quickly uncover the most promising candidates, predict how they will work, and foresee which patients will best respond? What if researchers could apply the vast volumes of data available in both public and proprietary sources in order to make decisions on where to focus their efforts and budget?

While these are lofty goals, investors are betting big that AI will deliver.

Investment has boomed in recent years. Third-party investment in AI-enabled drug discovery was estimated to have reached $5.2 billion in 2021.6

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Pharma Embraces Artificial Intelligence

Overarching Goals for AI in Pharma R&D The industry’s big bet on AI is rooted in big hope. Hope for better treatments, faster development, and reduced costs. Let’s explore each.

Better Treatments

Companies are leveraging AI to improve their biology and chemistry methods, attain better success rates, and achieve cheaper and faster discovery processes.6 With AI, researchers can process billions of data points in unprecedented time and use sophisticated algorithms to uncover new connections or hidden patterns that might otherwise go undiscovered. For example, earlydiscovery researchers might use AI to more quickly identify and prioritize new compounds and targets, predict drug efficacy and side effects, or uncover new uses for existing treatments; in later development, AI might be used to manage the vast amounts of documents and data required to support any therapy in development, or to assess patient-treatment suitability for a more personalized approach to medicine.

Faster Development

AI can improve and accelerate the R&D process. As MIT Technology Review explains, “The basic steps involved in developing a new drug from scratch haven’t changed much.” For example, in small molecule drug design, researchers must still progress through target identification and validation, lead discovery and optimization, and drug development. AI doesn’t eliminate those steps, but rather improves inefficiencies, and it can do such to a greater degree than the computational tools researchers have been using for years.

According to MIT’s review, AI innovators are focusing on three main failure points within the drug discovery workflow: target selection, compound design, and patient-specific AI-Driven Pharma R&D

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profiling.3 By leveraging vast amounts of drug and molecular data to build complex models, researchers can shift early-discovery work from the lab to the computer, reserving experimental work for those candidates with greatest chances of success.

By some estimates, the use of AI could potentially condense a typical four- to five-year exploratory research phase into less than one year.5-6 D r u g -t o - m a r k e t s tat i s t i c s

Traditional Paradigm Estimates

AI-Guided R&D Projections

$2.6-6.7 billion

20-40% cost reduction or more

< 10 years

Several years faster

High failure rate

Lower failure rates

Reduced Costs

The potential cost savings from AI could be significant, and more importantly, those savings could translate into more treatment options. Insider Intelligence predicts huge payoffs, estimating cost reductions in the order of 70%.7 Morgan Stanley analysts estimate that 20% to 40% cost savings within preclinical development could help fund the successful development of four to eight novel molecules.8 They believe that, over a 10-year period, even “modest improvements in early-stage-drugdevelopment success rates enabled by the use of AI and ML could lead to an additional 50 novel therapies…and more than $50 billion opportunity.”

Pharma Embraces Artificial Intelligence

Evidence of AI’s Growing Impact in Pharma Steady investment in AI is already showing its impact on the market: Biotechs using AI-first approaches have more than 150 small-molecules in discovery and more than 15 in trials.6 The United States Food and Drug Administration (FDA) reports a rapid increase in the number of small molecule drug and biologic submissions that use some element of AI/ML, with more than 100 submissions in 2021 alone.11 Collaborative initiatives are helping foster AI-based innovation. Scientific communities that have traditionally been small, isolated, and protective of their proprietary data, are now joining forces to share insights and securely leverage pooled data to improve models. For example: AlphaFold is a neural-network program that uses publicly-available protein structures to solve the decades-long challenge of predicting protein-structure folding, which can help propel efforts to understand protein function and disease mechanisms, design new drugs, and develop synthetic proteins.12,13 DeepTracer-Refine is an automated refinement tool that improves upon AlphaFold predictions by overcoming issues with folding regions between domains.1 MELLODDY (Machine Learning Ledger Orchestration for Drug Discovery) leverages the world’s largest collection of small molecules with known biochemical or cellular activity to enable more accurate predictive models and increase efficiencies in drug discovery.15 Specifically, the project works to enhance predictive machine-learning models on decentralized data from ten pharmaceutical companies, without exposing proprietary information

The FDA has even introduced a new AI-based safety-evaluation tool to help demonstrate biosimilarity.16

AI innovators are beginning to deliver results. For example: Exscientia was one of the first companies to use AI to bring drugs to trial in rapid fashion; its success is garnering the addition of big pharma, like Sanofi, who are increasingly turning to partnerships with AI innovators to spur innovation.17-19

Insilico Medicine, an end-to-end AI-driven biotech, made a big splash in June 2023, when its cancer drug, ISM3091, received approval for phase 1 trials and its chronic-lung-disease treatment, INS018_055, entered Phase II patient trials for idiopathic pulmonary fibrosis.20,21 Rembarkly, the discovery process for INS018_055, which included both AI-based target discovery and AI-guided design, began in just 2020.20,2 AbSci Corporation announced in early 2023 that it successfully developed validated de novo antibodies using AI, and the company projected that this milestone could potentially help reduce the time it takes for new therapies to reach the clinic from six years to 18-24 months.3,22

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Pharma Embraces Artificial Intelligence

Evolution of AI Researchers can collect and process data at an unprecedented scale thanks to advances in technology and laboratory instrumentation.

To many experts, it may feel like much of this buzz

Generative AI is the next step toward fully applying

around AI in pharma R&D focuses on an already-

existing knowledge to accelerate innovation.

familiar concept—predictive modeling and

Companies are applying it in a variety of ways to

analytics. For years, researchers have supplemented

help further streamline notoriously inefficient,

experimental work with in silico design strategies,

highly-iterative R&D processes.

such as virtual screening, modeling and simulation, and property-calculations. While undoubtedly helpful, these piecemeal strategies have not truly revolutionized the overall drug discovery process.

Generative AI is sparking hope for a bigger change.

A prime example is de novo drug design, where generative AI first uses existing structural data, such as for small molecule compounds or therapeutic protein sequences, to design promising novel structures (as opposed to just evaluating

Unlike predictive models that process data to make

structures that already exist in a database). AI can

predictions, generative AI uses existing data to

then assess those candidates for key properties,

actually create data, whether that be new text, code,

such as bioactivity or potential side effects and

23-25

graphics, or even novel drug designs.

While

large-language-models (LLMs) like ChatGPT are garnering a ton of attention, there are many other types of generative models, including neural networks (e.g. , generative, graph, recurrent) and other machine-learning and deep-learning algorithms (e.g. , random forest, support vector machine, variational autoencoders, reinforcement

interactions, thus helping to reduce the costly trial23,28

and-error so common in drug discovery.

The possibilities for generative AI seem endless. It is being used to map disease pathways, suggest potential disease targets, create code to automate tasks like annotation, summarize collections of publications, generate documentation and reports, 23-25

predict clinical trials outcomes, etc.

But while

24,27

learning, transfer learning).

generative AI holds incredible potential, it is not a

Many companies, like those cited in our previous

panacea; rather, as we discuss in the coming

examples, are employing sophisticated AI-driven

sections, it depends on large volumes of high-quality

drug-discovery platforms that marry generative AI

training data, reliable models, and data-science

techniques with data analytics, modeling and

experts who are adept at interpreting and applying

23-26

simulation, and supercomputing.

In fact,

generative AI is rapidly grabbing ahold of the industry and it is expected to reach a market size of 26

more than $160 million in 2023.

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outputs.

Pharma Embraces Artificial Intelligence

These advances in AI signal that a fundamental change is underway.

In fact, experts project a huge transformation in the industry over the next several years.3,22 IDC predicts that by 2026 “in silico” first strategies will overtake traditional early benchbased research at major pharmaceutical companies.9-10 A key reason we are seeing such swift progress is because AI models are now more easily built thanks to automation and technology advances that are creating an abundance of available training data (e.g., chemical and molecular data, experimental data, genomic data, health-record data, scientific publications, etc).

As AI becomes a reality that impacts every aspect of the industry, the status quo will undoubtedly shift—from how early-discovery researchers uncover targets and develop treatments, to the ways regulatory agencies approach approvals, to how providers treat patients. Preparing for that shift will be essential to success.

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Key Opportunities for AI in Pharma R&D “Artificial intelligence (AI) and machine learning (ML) are no longer futuristic concepts; they are now part of how we live and work...

AI/ML’s growth in data volume and complexity, combined with cutting-edge computing power and methodological advancements, have the potential to transform how stakeholders develop, manufacture, use, and evaluate therapies.

Ultimately, AI/ML can help bring safe, effective, and high-quality treatments to patients faster.”

Patrizia Cavazzoni, M.D. Director of the FDA’s Center for Drug Evaluation and Research11

Early use of AI within pharma largely involved companies developing small molecules, which lend themselves to AI because they’re relatively simple compared to biologics, and also because there are decades of data upon which to build models.17,29-30

Even then, the availability of sufficient training data and the complexity of models led to great variance in the ease of adopting AI across early small molecule drug discovery; some types of models, such as early screening and physical-property prediction, were easier to implement than others, like target prediction and toxicity assessment.31-32

The application of AI in biology research has quickly gained ground. Biologics R&D teams are increasingly turning to AI for help with disease and target understanding, as well as entity design and optimization.33 As of late 2022, anywhere from 50 to 60 AI-enabled biologics were in different stages of development.33

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e

ortunities for AI in Pharma R&D

K y Opp

Now that AI has gained its footing in the industry, companies are beginning to apply it within wide-ranging areas of R&D (Table 1).

With AI, teams across the entire product lifecycle can quickly make data-driven decisions that help them allocate resources, budgets, and expertise where they will be most impactful.

T a b l e 1 : Key areas

of

A

I A p p l i c a t i o n i n t h e P h a r m a c e u t i c a l M a r k e t 5 , 1 7, 3 3 - 3 9

Traditional Paradigm Estimates

Later Phase Uses

Target Identification, Selection, and Prioritization Disease pathway mappin Novel target identificatio 3D protein structure predictio Epitope selectio Data-driven target selection (multi-omics, phenotype and expression, publications and patents, etc.

SAR predictio Drug-target modeling and molecular simulation Binding-site-modifications analysis

Development Crystal-structure predictio Stability and shelf-life modelin Biologics developability assessment (e.g., viscosity, aggregation, degradation)

Screening, Design, Optimization, and Prioritization Advanced compound screening (e.g., dualbinding compound searching Biologic repertoire optimizatio Chemical property predictio Bioactivity predictio Off-target effect predictio ADMET/Tox and PK/PD predictio Immunogenicity and humanization predictio Physio-chemical property prediction (e.g., solubility, aggregation Candidate prioritizatio Optimal synthetic route selectio Drug repurposing analysi De novo desig Patent checking

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Trial Management Data-driven trial desig Endpoint assessmen Patient recruitment and selection/stratificatio Dosing optimizatio Adherence monitorin Data collection, management, and analysis Post-marketing Surveillance Case processing, evaluation, and submission Manufacturing Process design and scale-u Advanced process contro Process monitoring and fault detectio Asset and supply-chain managemen Trend monitoring (e.g., complaint processing, division reports)

Key Challenges for AI in Pharma R&D Strategy, Technology, and Expertise Data Challenges Model Concerns

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Key Challenges for AI in Pharma R&D

Strategy, Technology, and Expertise Companies looking to integrate AI into their existing R&D workflows—whether on their own or with the help of a partner— need a clear vision on where, why, and how they will use AI. According to Deloitte, identifying business cases with the highest value is the top challenge impacting companies’ AI initiatives (followed by data challenges and successfully integrating AI into the organization).40 But strategic planning is often a big hurdle that is complicated by the growing potential role AI can play across R&D. Some experts advise focusing on a half-dozen or so specific use cases that are spread across different programs or stages of discovery.6 And once priorities are set and plans are in motion, patience is key because it can often take longer than expected to see returns because of the time it takes to train models.

Technology struggles present another key challenge to applying AI in pharma. Innovation with AI typically demands that companies manage their data and workflows differently than they have in the past.

Often, the legacy technology infrastructures in place cannot accommodate the level of integration and data fluidity needed for leveraging AI.41 In fact, nearly 30% of the biopharma and medtech companies that Deloitte surveyed said data struggles, such as data quality, siloed data systems and legacy-system integration, negatively impact their AI initiatives.40

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Another key challenge in implementing AI can be skill gaps. While scientists have long been perceived and portrayed in film as old people in white lab coats perched at a bench full of bubbling fluorescent liquids, the present day reality is quite different.

Data science has become just as important as science data, and the degree to which a company can bridge those two worlds will greatly impact the success of their AI initiatives. Yet, many companies report that it is still difficult to find talent who have industry expertise and strong AI skill sets.40

Key Challenges for AI in Pharma R&D

Data Challenges There is no AI without data. Models are built on real-world data and that data must be plentiful, high-quality, and machine-ready.

When adopting AI into drug discovery, issues of data bias, integrity, privacy and security, provenance, relevance, replicability, reproducibility, and representativeness are of top concern.35 Companies aiming to utilize their experimental data for AI must ensure they have the right data-management processes in place because, as described below, AI relies on sufficient, machine-ready data.

Sufficient Data: AI requires good data, and lots of it.

Some experts estimate that, in order for a machinelearning model to be reliable, it takes around two to three years of historical training data.42 But good data can be hard to come by. Publicly available data can be inadequate, forcing companies to rely on their own experimental data and domain knowledge.

This can be exceptionally challenging for companies that have grown via mergers and acquisitions without ever integrating their data or systems.

Companies looking to leverage AI need to first adopt data-management tools and workflow processes that not only facilitate error-free data capture, but also handle the volume and variety of data produced by different teams and partners, who may be following different workflows and using a wide variety of speciality technologies.

Machine-Ready Data: Experimental data collected for use as training data, or within models, also need to be machine-ready.43 Companies must promote processes that prioritize proper data labeling and annotation and they must adopt infrastructure that keeps data accessible and interoperable. Beyond being machine-ready, data must be trustworthy and reliable. Checks-and-balances should be built into experimental R&D workflows to confirm the accuracy and integrity of data that are being captured. This will ensure that the quality of the data going into, and out of, models is sufficient.

“The main challenge to creating accurate and applicable AI models is that available experimental data is heterogenous, noisy, and sparse, so appropriate data curation and data collection is of the utmost importance.” 41 Expert Opinion on Drug Discovery review

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Key Challenges for AI in Pharma R&D

Model Concerns All models, like all data, aren’t created equal.

As AI becomes more of a mainstay in drug discovery and development, questions regarding the quality, reliability, and limitations of models are unavoidable.35

Is the quality and volume of data used for training sufficient for the model’s purpose?

How does the model avoid perpetuating quality issues and diluting the truth?

Are the questions being asked too complex for the model’s degree of specificity?

Do the results need secondary assessment or scrutiny?

Regulatory agencies are working to keep pace with AI’s momentum and are in the early phases of creating regulatory frameworks that support AIdriven innovation, while still ensuring the safety and efficacy of drugs reaching patients. In the United States, the FDA has engaged key stakeholders, including those in industry, academia and patient advocacy, to gather insights into current and planned use of AI, which it will use to help inform future regulation.

The agency recently published two key discussion papers: “Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products” and “Artificial Intelligence in Drug Manufacturing.”34-35 In Europe, the European Medicines Agency recently published a reflection paper on the use of AI in the medicinal product lifecycle, aiming to provide guidance for the safe and effective use of AI/ML in the development of medicines.44

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Key Challenges for AI in Pharma R&D

Key areas of concern regarding the use of AI/ML in drug development include: Transparency, Credibility, and Explainability

Researchers should be able to understand the applicability of the models they have been employing and trust that those models were sufficiently trained using accurate, relevant, and properly sourced data. However, as noted in the FDA’s discussion document, the complexity of models can make their explainability challenging and proprietary concerns may impede the disclosure of certain details.35

Bias Avoidance

Models are only as good as their training data and adequate data diversity is needed to avoid bias. In its reflection paper on the use of AI in the medicinal product lifecycle, the European Medicines agency explains, “AI/ML models are intrinsically data-driven, as they extract their weights from training data.44 This makes them vulnerable to the integration of human bias into models. All efforts should be made to acquire a balanced training dataset, considering the potential need to over-sample rare populations, and taking all relevant bases of discrimination… into account.”

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Performance and Reliability

While definite, confident answers may be attractive, those answers mean little without insight into training-data sourcing, disclosure of a model’s performance and reliability/uncertainty, as well as some degree of traceability and auditability.

Developers of AI tools should aim to build semantic relationships into neatly organized training data and provide interpretable metrics that allow users to gauge results with confidence; users should not be expected to blindly take predictions at face value.

Specificity

Generalized models that have been trained on huge datasets with no specificity will undoubtedly struggle in specialist areas. For example, in drug discovery, if a predictive algorithm has been trained on small molecules for protein-drug binding, the trustworthiness of its binding predictions depends on how structurally similar the input molecules are to the molecules in the training set. In such cases, an uncertainty metric can help improve transparency, letting users know the limitations of the model.

Getting AI-Ready with Dotmatics

Companies are turning to AI to accelerate the fundamental shift toward data-driven R&D that has been happening for several years.43 Many are pouring tens of millions of dollars into AI initiatives in hopes of attaining faster and more cost-effective innovation.40 Unfortunately, many companies face a harsh reality—their data are ill-suited for utilization in AI. The root causes are wide-ranging: poor data access, siloed data, lack of standardization, inefficient annotation, questionable integrity, limited traceability, ill-suited legacy data-technology infrastructure, and so on and so forth.40

The problem is quite pervasive. In a recent survey, nearly 30% of biopharma and medtech R&D leaders questioned admitted that data struggles negatively impact their AI initiatives. In particular, poor quality data and siloed data systems were top points of concern.40

Companies facing such issues will undoubtedly struggle to reap the benefits of AI until their data-management systems and processes are improved.

Dotmatics can help.

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Getting AI-Ready with Dotmatics

TM

Dotmatics Luma

is a revolutionary scientific-data platform

that helps scientists and administrators unify and analyze large volumes of data for better decision-making.

Luma provides an out-of-the-box, low-code, SaaS platform that flexibly aggregates all relevant data into intelligent data structures; this enables clean, reliable data analysis and paves the way for meta-analysis and AI and ML-based algorithms.

Luma helps companies circumvent all the most common obstacles that occur when trying to ready R&D infrastructures and data for AI, including:

Varied Data Producers

Instrument Isolation

Luma can ingest data from a variety of

Luma takes on the onus of managing data

producers, including ELNs, instruments in the

outputs from disparate instruments, even

lab, and data coming from a wealth of

when those outputs are encrypted or non-file

sources, such as animal studies, clinical

based. Luma parses out descriptive metadata

studies, material registries, or other scientific

and experiment results and it harmonizes data

systems. It automates repetitive data-

from instruments in the same instrument class.

management steps wherever possible.

Data Accessibility and Usability Scientific Application Diversity

Luma maximizes the value of R&D data via a

Luma provides low-code app-building

data-value chain that progressively enriches,

functionality, so no matter how diverse the

correlates, and contextualizes data and

needs across an organization, it’s easy to

prepares it for more advanced data processing

build applications that share a common

and enrichment activities. Its API-first

design pattern and data model.

approach supports on-demand access to data when and how it is needed.

Data Volumes and Silos

Luma handles the volume and complexity of

With an R&D infrastructure and

diverse types of scientific data at exponential scale. It breaks down silos that are so common in scientific research with an agile data modeling approach that separates the physical model from the logical model; with Luma, companies can model, store, process, and extract data within a governance framework they define.

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data foundation built on Dotmatics Luma, companies are ready to begin their AI journey.

Getting AI-Ready with Dotmatics

Preparing Data for AI with Dotmatics

Teams cannot begin their AI journey without a robust data infrastructure. Dotmatics connects all of a company's R&D data in one place and can integrate with the most popular applications that scientists use to generate and analyze R&D data.

More than two million scientists working across scientific disciplines including chemistry, biology, and chemicals and materials, trust Dotmatics to help them: Capture clean and trustworthy research data, such as through automated instrument-data collection, database and application integration, and errorproof data entry via electronic laboratory notebooks (ELNs) that feature both standardized and flexible protocol templates, forms-based entry, data crosschecking, and sign-off capabilities.

Remove data silos by seamlessly integrating all the diverse data that make up the experimental fabric, including all the chemistry, biology, formulation and physical characterization data that researchers are creating using a wide-range of specialty tools, as well as data created by external partners, and data available within the public domain.

Provision the model-quality, machine-ready data that is needed for AI by breaking away from proprietary data formats, standardizing data, automating QC and QA, and eliminating time-consuming and error-prone data wrangling.

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Getting AI-Ready with Dotmatics

Table 2 highlights key ways Dotmatics prepares experimental data for AI. Ta b l e 2 : K e y S t e p s T o wa r d P r e p a r i n g E x p e r i m e n ta l D ata f o r A I

matics helps ensure experimental R&D data are properly:

Dot

Captured

Clean and fast data collection from all data producers, whether through automated instrument data capture, database integration, or error-proof data entry via ELNs.

Labeled

Detailed metadata and naming conventions ensure easy correlation back to specific users, compounds, sequences, projects, CROs, etc.

Standardized

Standardized data model breaks away from proprietary data formats, automates QC and QA, and provisions the model quality data needed for in-depth scientific analyses.

Centralized

Scientifically-aware master repository eliminates disconnected (often discipline-specific) data silos and creates a single source of truth for all users and collaborators across an organization and its partners.

Interoperable

Seamless data exchange not only amongst behind-the-scenes systems and applications, but also amongst end users via research collaboration tools that help different teams talk to each other, share data and insights, collaborate, and move projects forward.

e ible and Traceable

Acc ss

elatable

ast, constant, and traceable access to data, such as through scientific search, reports and dashboards, and browseable experiments.

F

terative data layering with records that are layered with data points from multiple (often cross-team) analyses, which provides a complete picture that accurately reflects context.

R

I

Secure

Safeguarded data, at rest and in transit, with granular authorization and access controls, which make it possible for different users, collaborators, and CROs to work on (and exchange data within) the same platform, without threatening privacy.

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Getting AI-Ready with Dotmatics

Phased AI Adoption with Dotmatics Adopting AI into an existing drug discovery program is often a multi-step journey, as illustrated in Figure 1 and described below.

Early Adoption The first essential step toward adopting AI is clean data. This sounds simple, but the reality for many pharma companies is that they have mountains of diverse data— mindblowing figures, like 50 petabytes of data. And it’s stored in all types of locations that can’t share effectively between each other, including various software, instruments, spreadsheets, etc.

Once that data is organized, it can be used to do useful things like run reports and adhoc queries, which help scientists begin to more easily analyze and understand things. Using interactive visualization is the next level of that process because it enables scientists to actually see patterns within their own drug discovery data.

Mid Journey From there we start getting into the real possibilities of AI with things like predictive modeling and prescriptive analytics. Imagine if AI could discover that patients with a specific genetic mutation have a higher likelihood of responding positively to a certain class of drugs; that would lead to personalized treatment plans that could significantly improve patient outcomes and reduce adverse reactions. Predictive modeling and prescriptive analytics make this possible.

Late Phases Ultimately, you’re maximizing the full power of AI when you reach the point of automated decision making, where AI is able to do all kinds of exciting things— autonomously identify patterns, trends, and correlations within the drug discovery data, generate predictions, provide recommendations for potential drug candidates, optimize experimental designs, and even propose novel hypotheses for further investigation.

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Companies who choose Dotmatics Luma to support their R&D data foundation are well prepared for leveraging the power of AI, whether that be creating generative AI query-building options or using predictive and adaptive AI to augment lab testing and procedures.

Figure 1 Adding AI into a drug-discovery program is typically a multi-step journey that begins with implementing necessary data-management systems and processes that are essential to readying data for AI.

Early Adoption See patterns within R&D data Reports Adhoc queries Interactive visualization

AI-Ready Data

Mid Journey AI-guided R&D Predictive modeling Prescriptive analytics

Dotmatics Platform

Late Phases Automated decision making Autonomous pattern recognition Prediction generation Hypotheses proposal

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AI-Enabled Solution Spotlights Beyond our AI-ready scientific-data platform, Luma, Dotmatics works closely with our customers to see where targeted application of AI can best help accelerate R&D innovation.

Here are a few scenarios where AI is improving our customers’ workflows and decision making:

AI-Based Automated Flow Cytometry Gating Dotmatics has crowd-sourced the training of ML algorithms available within its flow cytometry software, OMIQ. Flow cytometry is an essential lab technique that uses lasers to rapidly evaluate high volumes of cells against multiple parameters. It has broad application in scientific research, clinical analysis, and treatment development.

The process results in a huge amount of raw data that must be turned into actionable insights. This has long been done through a manual process where analysts draw gates around data points to identify cells of interest or concern. They then progressively home in on cell populations of interest.

Dotmatics has built an ML-powered technique called autogating that uses open data to help teach the software to automate this process. This will open the door to even faster, less expensive, and more significant medical breakthroughs.

AI Constructed Drug-Candidate-Review Visualizations Dotmatics Vortex is a data visualization and analysis program that helps with R&D decision making. It can leverage AI/ML to transform data that would typically be displayed across spreadsheets and tables into easy-to-interpret visualizations that make drug-candidate review easier. For example, AI/ML can be used to fold the chemical space to quickly highlight areas of success and failure.

AI-Powered Protein Analysis Protein Metrics allows scientists to use data generated on analytical instruments, such as mass spectrometers or chromatography machines, to quickly identify and report on protein sequences. The program features targeted use of AI/ML to help with tasks such as protein searching, identification, and scoring.

AI-Driven Pharma R&D

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Advance Your AI Journey with Dotmatics Request a demonstration to see how Dotmatics can help advance your AI journey.

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References

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