Valuation: Exscientia plc

Market Cap 500M 681M 584M 546M 938M 65.18B 950M 6.45B 2.52B 32.74B 2.56B 2.5B 108B P/E 2022
-4.55x
P/E 2023 -4.28x
Enterprise Value 156M 212M 182M 170M 292M 20.31B 296M 2.01B 784M 10.2B 797M 780M 33.74B EV / Sales 2022
1.9x
EV / Sales 2023 14.2x
Free-Float
36.87%
Yield 2022 *
-
Yield 2023 -
3 years 3.8
Extreme 3.8
7.91
5 years 3.8
Extreme 3.8
30.38
10 years 3.8
Extreme 3.8
30.38
Manager TitleAgeSince
Chief Tech/Sci/R&D Officer 61 05/06/2024
Chief Tech/Sci/R&D Officer - 30/04/2022
Chief Tech/Sci/R&D Officer - 30/04/2024
Director TitleAgeSince
Director/Board Member 63 27/09/2017
Director/Board Member 43 30/04/2020
Chairman 61 11/02/2024
Change 5-day change 1-year change 3-year change Capi.($)
+3.20%-.--%-.--%-24.02% 633M
+1.09%+3.39%+3.50%+25.49% 51.22B
-1.07%+2.82%+474.18%+601.24% 45.38B
+1.44%+7.77%+19.27%+90.94% 41.97B
+1.47%+9.79%+40.15%+21.21% 42.15B
-0.93%-4.80%+17.39%+46.60% 31.95B
-0.60%+1.93% - - 16.73B
+1.32%+7.30%+36.93%+152.36% 17.1B
+0.03%+3.91%+5.84%+35.04% 14.76B
+1.28%+5.41%+91.01%+46.93% 13.91B
Average +0.72%+2.87%+76.47%+110.64% 27.58B
Weighted average by Cap. +0.47%+3.12%+103.21%+148.80%

Financials

2022 2023
Net sales 27.22M 37.1M 31.78M 29.74M 51.09M 3.55B 51.76M 351M 137M 1.78B 139M 136M 5.9B 20.08M 27.37M 23.44M 21.94M 37.68M 2.62B 38.18M 259M 101M 1.32B 103M 101M 4.35B
Net income -119M -162M -139M -130M -223M -15.48B -226M -1.53B -598M -7.78B -608M -594M -25.73B -146M -199M -170M -159M -274M -19.04B -278M -1.88B -735M -9.56B -747M -731M -31.63B
Net Debt -492M -670M -574M -537M -923M -64.15B -935M -6.35B -2.48B -32.23B -2.52B -2.46B -107B -344M -469M -402M -376M -646M -44.88B -654M -4.44B -1.73B -22.54B -1.76B -1.72B -74.57B
Logo Exscientia plc
Exscientia PLC is a drug design and development company. The Company combines precision design with integrated experimentation to invent and develop drugs. It uses artificial intelligence (AI) in drug discovery to progress AI-designed small molecules into a clinical setting. It uses the patient's tissue data to define optimal profiles for research, improve experimental assessment during design and improve outcomes in a medical setting. It has developed an internal pipeline focused on oncology, while its partnered pipeline extends to various other therapeutic areas. It combines genetic data and global literature in machine learning models to anticipate and confirm disease-target associations. The Company's experimental platform records responses in real patient samples allowing it to generate high-precision views of potential patient response. Its product pipeline consists of GTAEXS617, EXS4318, EXS74539, and EXS73565.
Employees
483
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