Insilico Medicine Scientists Propose Stricter Standards for Evaluating Generative AI-Produced Molecules

Insilico MedicineA new microperspective in the ACS journal Medicinal Chemistry Letters evaluates recent research on artificial intelligence (AI)-generated molecular structures from the point of view of the medicinal chemist and recommends guidelines for assessing the novelty and validity of these compounds. The perspective, published as part of the journal's virtual special issue "New Enabling Drug Discovery Technologies - Recent Progress," provides an analysis of eight molecular structures produced from generative chemistry published in the past two years to reveal the impact of AI and machine learning (ML) methods on modern-day drug discovery. In total, the authors found 55 recent publications covering generative chemistry efforts.

Designing synthetically feasible molecular structures that are novel and experimentally valid in the context of the disease is a challenge for generative chemistry algorithms. "We hoped to provide an in-depth analysis of the strengths of certain AI and ML generative chemistry approaches to produce truly novel and synthetically feasible molecular structures," says Alex Aliper, Ph.D., President of Insilico Medicine, who co-authored the study.

Rather than simply focusing on AI-generated structures, the authors examine the validity of these structures from the medicinal chemist's perspective - including synthesis and biological assessment.

Ultimately, say the Insilico scientists, as terms like "generative AI" and "generative chemistry" become more widespread, it’s essential to define relevant terms better and demonstrate the validity of generated structures across various measures. Their recommendations include:

  • Thoroughly inspecting generated structures in regards to their novelty and patentability.
  • Using rationally balanced preprocessing rules and medicinal chemistry filters adapted for generative pipelines.
  • Avoiding misleading statements, especially “novel drug candidate” and “novel lead compounds,” which must be supported with exhaustive biological data. In many cases, “primarily hit compound” is the only term that can be reasonably applied for active compounds of generative origin.
  • Employing severe similarity metrics.
  • Providing medicinal chemists with all generated structures besides those presented by authors as the most promising ones.
  • Evaluating active molecules of AI origin at least using standard MTS or MTT assays to avoid nonspecific action and cytotoxicity.
  • Assessing synthetic accessibility.
  • Improving the generative engine, with more attention to the training set, the test set, and similarity metrics.
  • Paying more attention to reinforcement learning with advanced systems and processes intended to rapidly evaluate the generated molecules for desired properties.

"We are encouraged by the increasing use of generative AI in chemistry which can help speed and expand drug discovery efforts," says Alex Zhavoronkov, PhD, founder and CEO of Insilico Medicine and co-author of the paper. "But we believe that publications in generative chemistry should always include experimental validation and rigorous evaluation and review by medicinal chemists. We think the process can be further improved by introducing new techniques to generate and evaluate the novel molecular structures from a medicinal chemistry perspective to produce the next generation of novel AI-generated drugs."

About Insilico Medicine

Insilico Medicine, a clinical-stage end-to-end artificial intelligence (AI)-driven drug discovery company, is connecting biology, chemistry, and clinical trials analysis using next-generation AI systems. The company has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques to discover novel targets and to design novel molecular structures with desired properties. Insilico Medicine is delivering breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system (CNS) diseases and aging-related diseases.

Ivanenkov Y, Zagribelnyy B, Malyshev A, Evteev S, Terentiev V, Kamya P, Bezrukov D, Aliper A, Ren F, Zhavoronkov A.
The Hitchhiker's Guide to Deep Learning Driven Generative Chemistry.
ACS Med Chem Lett. 2023 Jun 30;14(7):901-915. doi: 10.1021/acsmedchemlett.3c00041

Most Popular Now

AI Predictions for Colorectal Cancer: On…

Colorectal cancer (CRC) ranks second in leading causes of cancer-related deaths globally, according to the WHO. For the first time, researchers from Helmholtz Munich and the University of Technology Dresden...

Combining AI Models Improves Breast Canc…

Combining artificial intelligence (AI) systems for short- and long-term breast cancer risk results in an improved cancer risk assessment, according to a study published in Radiology, a journal of the...

ChatGPT Shows 'Impressive' Acc…

A new study led by investigators from Mass General Brigham has found that ChatGPT was about 72 percent accurate in overall clinical decision making, from coming up with possible diagnoses...

Healthcare Chatbot: Expand Support with …

The Danish eHealth platform, sundhed.dk, has faced a substantial surge in requests from Danish citizens and has swiftly expanded its support and effectively adapt to the ongoing changes in queries due...

WiFi SPARK's Healthcare Business Re…

Leading WiFi provider WiFi SPARK is rebranding its healthcare arm as SPARK Technology Services Limited. The new identity marks the completion of the integration of the former Hospedia bedside unit...

ChatGPT is Debunking Myths on Social Med…

ChatGPT could help to increase vaccine uptake by debunking myths around jab safety, say the authors of a study published in the peer-reviewed journal Human Vaccines and Immunotherapeutics. The researchers asked...

Online AI-Based Test for Parkinson'…

An artificial intelligence (AI) tool developed by researchers at the University of Rochester can help people with Parkinson's disease remotely assess the severity of their symptoms within minutes. A study...

AI Performs Comparably to Human Readers …

Using a standardized assessment, researchers in the UK compared the performance of a commercially available artificial intelligence (AI) algorithm with human readers of screening mammograms. Results of their findings were...

Siemens Healthineers Expands Production …

Siemens Healthineers is expanding its site in Rudolstadt, Germany. By mid 2024, a new manufacturing building will be built on the site. The new manufacturing plant will produce electron accelerators...

More Cases of Breast Cancer Detected wit…

One radiologist supported by AI detected more cases of breast cancer in screening mammography than two radiologists working together, reports the ScreenTrustCAD study from Karolinska Institutet in The Lancet Digital...

MEDICA 2023 + COMPAMED 2023: "Where…

13 - 16 November 2023, Düsseldorf, Germany. The medical technology market is in worldwide motion and the signs ahead of MEDICA 2023 and COMPAMED 2023 in Düsseldorf as the internationally leading...

Smartphone Technology Expected to Advanc…

Since the 1980s, we have known that neurological soft signs (NSS) can distinguish people with schizophrenia from psychiatrically healthy individuals. NSS are subtle neurological impairments that principally manifest as decreased...