“Artificial Intelligence in Drug Discovery: What Is Realistic, What Are Illusions? Part 1: Ways to Make an Impact, and Why We Are Not There Yet: Quality Is More Important Than Speed and Cost in Drug Discovery”, Andreas Bender, Isidro Cortés-Ciriano2021-02 (, )⁠:

We first attempted to simulate the effect of (1) speeding up phases in the drug discovery process, (2) making them cheaper and (3) making individual phases more successful on the overall financial outcome of drug-discovery projects. In every case, an improvement of the respective measure (speed, cost and success of phase) of 20% (in the case of failure rate in relative terms) has been assumed to quantify effects on the capital cost of bringing one successful drug to the market. For the simulations, a patent lifetime of 20 years was assumed, with patent applications filed at the start of clinical Phase I, and the net effect of changes of speed, cost and quality of decisions on overall project return was calculated, assuming that projects, on average, are able to return their own cost…(Studies such as [33], which posed the question of which changes are most efficient in terms of improving R&D productivity, returned similar results to those presented here, although we have quantified them in more detail.)

It can be seen in Figure 2 that a reduction of the failure rate (in particular across all clinical phases) has by far the most substantial impact on project value overall, multiple times that of a reduction of the cost of a particular phase or a decrease in the amount of time a particular phase takes. This effect is most profound in clinical Phase II, in agreement with previous studies33, and it is a result of the relatively low success rate, long duration and high cost of the clinical phases. In other words, increasing the success of clinical phases decreases the number of expensive clinical trials needed to bring a drug to the market, and this decrease in the number of failures matters more than failing more quickly or more cheaply in terms of cost per successful, approved drug.

Figure 2: The impact of increasing speed (with the time taken for each phase reduced by 20%), improving the quality of the compounds tested in each phase (with the failure rate reduced by 20%), and decreasing costs (by 20%) on the net profit of a drug-discovery project, assuming patenting at time of first in human tests, and with other assumptions based on [“When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis”, Scannell & Bosley2016]. It can be seen that the quality of compounds taken forward has a much more profound impact on the success of projects, far beyond improving the speed and reducing the cost of the respective phase. This has implications for the most beneficial uses of AI in drug-discovery projects.

…When translating this to drug-discovery programmes, this means that AI needs to support:

  1. better compounds going into clinical trials (related to the structure itself, but also including the right dosing/PK for suitable efficacy versus the safety/therapeutic index, in the desired target tissue);

  2. better validated targets (to decrease the number of failures owing to efficacy, especially in clinical Phases II and III, which have a profound impact on overall project success and in which target validation is currently probably not yet where one would like it to be [35]);

  3. better patient selection (eg. using biomarkers) [31]; and

  4. better conductance of trials (with respect to, eg. patient recruitment and adherence) [36].

This finding is in line with previous research in the area cited already33, as well as a study that compared the impact of the quality of decisions that can be made to the number of compounds that can be processed with a particular technique30. In this latter case, the authors found that: “when searching for rare positives (eg. candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (ie. a 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (eg. tenfold, even 100-fold) changes in models’ brute-force efficiency.” Still, currently the main focus of AI in drug discovery, in many cases, seems to be on speed and cost, as opposed to the quality of decisions.