Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes

Artem V. Artemov; Evgeny Putin; Quentin Vanhaelen; Alexander Aliper; Ivan V. Ozerov; Alex Zhavoronkov

doi:10.1101/095653

CARVIEW

MOTORHOMES

Select Language

HTTP/2 301 date: Sun, 18 Jan 2026 12:18:09 GMT content-length: 0 location: https://doi.org/10.1101/095653 server: cloudflare vary: Origin expires: Mon, 19 Jan 2026 12:18:09 GMT permissions-policy: interest-cohort=(),browsing-topics=() cf-cache-status: DYNAMIC nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} strict-transport-security: max-age=31536000; includeSubDomains; preload report-to: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=l38E1ShJLwWG25so3bB%2BDQsUeBCp1p470g4OO7QNc3zV4BNsmTyPgX2dwgUJc7bjR47Ck%2B9D19hnyE7N%2BIBbyIrZYJtKVA%3D%3D"}]} cf-ray: 9bfe088878b1af3d-BLR alt-svc: h3=":443"; ma=86400 HTTP/2 302 date: Sun, 18 Jan 2026 12:18:09 GMT content-type: text/html;charset=utf-8 location: https://biorxiv.org/lookup/doi/10.1101/095653 server: cloudflare vary: Origin vary: Accept expires: Sun, 18 Jan 2026 13:07:48 GMT permissions-policy: interest-cohort=(),browsing-topics=() cf-cache-status: DYNAMIC nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} strict-transport-security: max-age=31536000; includeSubDomains; preload report-to: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=hRDd%2FloGSYtLxiqhUnfqkmwXuQJi0gZ5rbgzUGrYihrrEO2HzTG6Tp%2F6Ppc3AC1ZxUM%2FdYNeaK9pU4c3t4qvvZIqtx%2Fmrg%3D%3D"}]} cf-ray: 9bfe0888c8f5af3d-BLR alt-svc: h3=":443"; ma=86400 HTTP/1.1 302 Found Date: Sun, 18 Jan 2026 12:18:10 GMT Content-Type: text/html; charset=iso-8859-1 Transfer-Encoding: chunked Connection: keep-alive server: cloudflare location: https://www.biorxiv.org/lookup/doi/10.1101/095653 cf-cache-status: DYNAMIC Nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} Report-To: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=%2FvHKkSygpuWN%2BI9dmECUjCbP752FPLwXksQoeqd9VtF6LsPKm5ArKoyYJVdRvIjBPxKrfj2lEoLidEwPA%2Fzu1wBdsvR56nQqSK9G"}]} CF-RAY: 9bfe08896f272ddf-BOM alt-svc: h3=":443"; ma=86400 HTTP/2 301 date: Sun, 18 Jan 2026 12:18:11 GMT content-type: text/html; charset=UTF-8 location: https://www.biorxiv.org/content/10.1101/095653v2 cf-ray: 9bfe088ca9388a2a-BLR x-content-type-options: nosniff x-content-type-options: nosniff x-drupal-cache: MISS expires: Sun, 18 Jan 2026 12:48:11 GMT cache-control: public, max-age=1800 pragma: no-cache vary: Accept-Encoding x-highwire-sitecode: biorxiv x-highwire-smart-code: biorxiv_production x-varnish: 695227372 via: 1.1 varnish x-varnish-ttl: x-varnish-cache: cf-cache-status: MISS set-cookie: __cf_bm=HsEcrNbPV0WvhFgri_FD0HbQEOclB3QWyoDRPnO5cBk-1768738691-1.0.1.1-Xdtrno1JejP7DOu2NuB0JxoGMoxb9vq2PvW_3B9oRe8DWIq6yR36KsZMQvOIC74Uz6MgSV4hgXALKXBxg3bi2AcAfNtHDnlS8diAOkBfI1I; path=/; expires=Sun, 18-Jan-26 12:48:11 GMT; domain=.www.biorxiv.org; HttpOnly; Secure; SameSite=None server: cloudflare HTTP/2 200 date: Sun, 18 Jan 2026 12:18:12 GMT content-type: text/html; charset=utf-8 content-encoding: gzip x-content-type-options: nosniff x-content-type-options: nosniff x-drupal-cache: MISS expires: Sun, 19 Nov 1978 05:00:00 GMT cache-control: no-cache, must-revalidate set-cookie: SSESS1dd6867f1a1b90340f573dcdef3076bc=5gq77_9U3ZBvAH0VvBWNcX19spNcLgIdNs268zt2Cmw; expires=Tue, 10-Feb-2026 15:51:31 GMT; path=/; domain=.biorxiv.org; secure; HttpOnly content-language: en x-frame-options: SAMEORIGIN x-generator: Drupal 7 (https://drupal.org) link: ; rel="canonical",; rel="shortlink" vary: Accept-Encoding x-highwire-sitecode: biorxiv x-highwire-smart-code: biorxiv_production x-varnish: 1893101331 age: 0 via: 1.1 varnish x-varnish-ttl: x-varnish-cache: cf-cache-status: DYNAMIC server: cloudflare cf-ray: 9bfe0894bac08a2a-BLR Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes | bioRxiv

Abstract

Despite many recent advances in systems biology and a marked increase in the availability of high-throughput biological data, the productivity of research and development in the pharmaceutical industry is on the decline. This is primarily due to clinical trial failure rates reaching up to 95% in oncology and other disease areas. We have developed a comprehensive analytical and computational pipeline utilizing deep learning techniques and novel systems biology analytical tools to predict the outcomes of phase I/II clinical trials. The pipeline predicts the side effects of a drug using deep neural networks and estimates drug-induced pathway activation. It then uses the predicted side effect probabilities and pathway activation scores as an input to train a classifier which predicts clinical trial outcomes. This classifier was trained on 577 transcriptomic datasets and has achieved a cross-validated accuracy of 0.83. When compared to a direct gene-based classifier, our multi-stage approach dramatically improves the accuracy of the predictions. The classifier was applied to a set of compounds currently present in the pipelines of several major pharmaceutical companies to highlight potential risks in their portfolios and estimate the fraction of clinical trials that were likely to fail in phase I and II.