Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with Multi‐Modal Artificial Intelligence

Abstract

Drug discovery and development is a notoriously risky process with high failure rates at every stage, including disease modeling, target discovery, hit discovery, lead optimization, preclinical development, human safety, and efficacy studies. Accurate prediction of clinical trial outcomes may help significantly improve the efficiency of this process by prioritizing therapeutic programs that are more likely to succeed in clinical trials and ultimately benefit patients. Here, we describe inClinico, a transformer‐based artificial intelligence software platform designed to predict the outcome of phase II clinical trials. The platform combines an ensemble of clinical trial outcome prediction engines that leverage generative artificial intelligence and multimodal data, including omics, text, clinical trial design, and small molecule properties. inClinico was validated in retrospective, quasi‐prospective, and prospective validation studies internally and with pharmaceutical companies and financial institutions. The platform achieved 0.88 receiver operating characteristic area under the curve in predicting the phase II to phase III transition on a quasi‐prospective validation dataset. The first prospective predictions were made and placed on date‐stamped preprint servers in 2016. To validate our model in a real‐world setting, we published forecasted outcomes for several phase II clinical trials achieving 79% accuracy for the trials that have read out. We also present an investment application of inClinico using date stamped virtual trading portfolio demonstrating 35% 9‐month return on investment.

nach0: multimodal natural and chemical languages foundation model

“…It is worth noting that the clinical trial domain holds particular significance for inClinico, a transformer-based artificial intelligence software platform designed to predict the outcome of phase II clinical trials. 10 The molecular generation task is relevant to the Chemistry42 platform. 44 …”

“…The application of neural network architectures and LMs has significantly advanced the field of chemistry, particularly in domain-specific information retrieval, drug development, and clinical trial design. 6–15 These developments include neural molecular fingerprinting, generative approaches to small molecule design, 11–13 prediction of pharmacological properties, and drug repurposing. 13,14 The clinical development of a drug is a time and money consuming process that typically requires several years and a billion-dollar budget to progress from phase 1 clinical trials to the patients.…”

FORWARD: A Learning Framework for Logical Network Perturbations to Prioritize Targets for Drug Development

“…We conclude that by recognizing which failed drugs Such training on both failed and successful programs is expected to iteratively refine the 34gene molecular signature of response, which can be retrained to adjust cut-offs in TI without altering CI that requires a simple majority. Although one such 'learning' model exists (81), it relies for target identification on conventional DEA and functional enrichment analyses (28,29); the latter is prone to biases (29,(82)(83)(84)(85)(86)(87)(88), i.e., skewed towards richly annotated genes instead of those with the strongest molecular data (82). The lead drug program from this model prioritized an anti-fibrotic target, TINK (TRAF2-and NCK-interacting kinase) and engaged it with an AIoptimized small molecule (INS018_055) for combating Idiopathic Pulmonary Fibrosis (IPF) (89).…”

The Future of ChatGPT in Medicinal Chemistry: Harnessing AI for Accelerated Drug Discovery

“…While ChatGPT can provide valuable insights and suggestions, it′s important to note that its recommendations should be validated by medical professionals and supported by rigorous scientific evidence [26] …”